GitHub Repository Popularity Predictor
ML Model Comparison & Predictions
Interactive Streamlit web application predicting GitHub repository popularity (Stars) using machine learning. Trained and compared 6 ML models (Linear, Ridge, Lasso, Random Forest, Gradient Boosting, XGBoost) on 215,000+ repositories. Features comprehensive exploratory data analysis, real-time predictions with scenario-based inputs, and deployed on Streamlit Cloud. Discovered critical insights: JavaScript/Python receive 3× more stars, MIT licensing shows 40% higher engagement, optimal repo size is 10-100 MB, active issue management correlates with 60% more forks.
Why it mattered
6 ML models trained; 215,000+ repositories analyzed; Real-time predictions with what-if scenarios; Deployed on Streamlit Cloud
What shipped
Interactive Streamlit web application predicting GitHub repository popularity (Stars) using machine learning. Trained and compared 6 ML models (Linear, Ridge, Lasso, Random Forest, Gradient Boosting, XGBoost) on 215,000+ repositories. Features comprehensive exploratory data analysis, real-time predictions with scenario-based inputs, and deployed on Streamlit Cloud. Discovered critical insights: JavaScript/Python receive 3× more stars, MIT licensing shows 40% higher engagement, optimal repo size is 10-100 MB, active issue management correlates with 60% more forks.
Proof signal
6 ML models trained; 215,000+ repositories analyzed; Real-time predictions with what-if scenarios; Deployed on Streamlit Cloud
System flow
EDA & Analysis -> Data Preprocessing -> Feature Engineering -> Model Training -> Model Comparison -> Real-time Deployment.
Honest maturity snapshot
Raw datasets, disconnected metrics, and hard-to-scan findings.
A structured analysis or dashboard with clear KPIs, visual patterns, and decision-ready insights.
Features
- Python integrated into the workflow.
- Pandas integrated into the workflow.
- NumPy integrated into the workflow.
- Scikit-learn integrated into the workflow.
- EDA & Analysis stage documented in the delivery flow.
- Data Preprocessing stage documented in the delivery flow.
Engineering challenges
- Keeping the implementation clear enough to explain while still solving the core technical problem.
- Choosing a scope that could be shipped, tested, and documented.
- Turning technical work into proof a visitor can evaluate quickly.
Results
- 6 ML models trained; 215,000+ repositories analyzed; Real-time predictions with what-if scenarios; Deployed on Streamlit Cloud
- Documented the engineering path and important learnings.
- Made source code or technical proof available for review.
What I learned
- Tradeoff learned: Keeping the implementation clear enough to explain while still solving the core technical problem.
- Proof learned: 6 ML models trained; 215,000+ repositories analyzed; Real-time predictions with what-if scenarios; Deployed on Streamlit Cloud
- Next iteration: Add richer screenshots or a narrated demo.