NBA Player Analysis

Description

I developed the NBA Player Analysis website as a fun side project. My friends and I always find ourselves debating and comparing players, so I figured this web app could help me win some arguments. I learned a lot while developing this website and was able to implement many really exciting features. Unfortunately, the website isn't currently deployed as I was having some trouble with the Redis cache, but please view the video above or pull the repository on GitHub to try it for yourself!

Tech Stack

Python

Django

JavaScript

HTML

CSS

Features

Ajax Search: Implemented an intelligent search feature using Ajax to provide real-time autofill suggestions.

Redis Cache: Enhanced performance and efficiency by integrating Redis for caching API responses.

Progression Graphs: Visualized player statistics through dynamic progression graphs using Matplotlib, Seaborn, and Pandas.

API Integration: Efficiently fetched and processed data from the NBA API to display comprehensive player statistics.

Web Scraping: Utilized Beautiful Soup for web scraping to supplement player information from various sources.

Theme Toggling: Implemented a toggle feature for light and dark themes to enhance user experience and accessibility.

RegEx: Applied Regular Expressions for robust and accurate data extraction and validation.