NBA Finals Prediction with Machine Learning

Project Overview

This project focuses on building a machine learning pipeline that can analyze historical NBA data and predict future playoff and Finals outcomes. The goal is to combine team performance, player information, roster composition, and advanced statistics into a structured dataset that can support predictive modeling.

The project is designed as an end-to-end data science workflow, beginning with data collection and database preparation and continuing through data cleaning, feature engineering, model training, evaluation, and visualization.

Data Sources

I am combining and cleaning information from two major Kaggle datasets:

• Basketball Database — includes team statistics, standings, game results, and historical season information.
• NBA Players Data — includes player-level information such as position, height, weight, draft details, and team affiliation.

Data Engineering and SQL Integration

The original datasets contain information across multiple files and structures. To make the data easier to query and analyze, I developed a Python workflow that loads the CSV files into a local SQLite database.

The current data pipeline is responsible for:

• Loading raw CSV data into structured database tables
• Standardizing team, player, and season information
• Joining player records with team affiliations
• Building complete team rosters for each season
• Preparing reusable datasets for later analysis and modeling

This database structure creates a more reliable foundation for feature engineering and allows the project to use SQL queries instead of relying entirely on repeated CSV processing.

Machine Learning Plan

After the data pipeline is complete, the next stage is to create features that describe team strength, roster quality, consistency, and playoff readiness.

Planned features include:

• Regular-season win percentage
• Offensive and defensive performance
• Player efficiency and roster depth
• Team experience and continuity
• Recent performance and momentum
• Historical playoff results

I plan to compare several classification models, including Logistic Regression, Random Forest, and XGBoost. Model performance will be evaluated using historical seasons that were not included in training.

Tools and Technologies

• Python
• SQL and SQLite
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• XGBoost

Next Steps

• Complete data cleaning and roster validation
• Create team-level and player-level features
• Train and compare multiple machine learning models
• Evaluate predictions using historical playoff results
• Build visualizations for team comparisons and playoff outcomes

This project reflects my interest in data engineering, predictive modeling, sports analytics, and building complete data products from raw information.