Project: NBA Finals Prediction with Machine Learning

Scenario

This project is a personal initiative to build a predictive model for NBA Finals outcomes based on historical player and team data. The goal is to integrate full rosters, regular season performance, and advanced statistics into a machine learning pipeline to forecast future playoff results.

I’m combining and cleaning data from two major Kaggle datasets:

• Basketball Database – includes team-level statistics, standings, and game results
• NBA Players Data – provides detailed player-level information like height, weight, draft info, and positions

📊 Data Pipeline & SQL Integration

Since both datasets were raw and unstructured, I wrote a Python script that uses SQLite to:

• Ingest CSV data into a local database
• Create dynamic SQL queries that join player info and team affiliations
• Build complete rosters for each team per season from 1996 onwards

This groundwork allows for future steps like feature engineering, win prediction modeling, and visual dashboarding using libraries like Pandas, Scikit-learn, and Matplotlib.

What’s Next?

• Feature engineering on advanced stats (e.g., efficiency, PER, team synergy)
• Train ML models (Logistic Regression, Random Forest, XGBoost)
• Cross-validation using historical finals outcomes
• Interactive visualizations for playoff trees and team comparisons

This project reflects my interest in sports analytics, end-to-end data pipelines, and actionable insights through modeling.