Fantasy Basketball Through API

This project, which I developed with a team, leverages data analytics and machine learning to optimize fantasy basketball team line ups. Specifically, through the use of Long Short-Term Memory (LSTM) models, our website predicts optimal player performances across various statistics including points per game, rebounds, assists, steals, blocks, and turnovers. 

The backend of our program uses web scraping and APIs to gather real-time data from player profiles and NBA game schedules, ensuring our database is complete. This data is then used to train the 6 LSTM models (for each category), allowing the site to forecast future performance. On the front-end, users can input their fantasy basketball team roster. The program will then analyze this input and output the top ten optimal lineups for the season, based on statistical analysis and player history, providing data-driven lineup recommendations.

This project demonstrates a practical application of our team’s ability to integrate and utilize machine learning technologies, helping fantasy basketball players enhance their decision-making process.