Project Overview
An interactive NBA basketball statistics explorer powered by Python and Streamlit. The app scrapes player performance data from Basketball Reference and presents it as a clean, filterable dataframe with rich data visualizations.
Key Features
- Scrapes NBA player stats directly from Basketball Reference
- Multi-select filters for team, year, and player position
- Intercorrelation heatmap visualization using Seaborn
- Downloadable CSV export of filtered data
- Interactive dataframe display with sortable columns
- Deployed as a Streamlit web application
What I Learned
This project taught me how to build data-driven web apps rapidly with Streamlit. Working with sports data introduced me to the full pipeline: scraping real-world data, cleaning it with Pandas, and presenting insights through interactive visualizations — all in a fraction of the time it would take with a traditional framework.