Live. Laugh. Lock. is a NBA bet tracking application that utilizes machine learning to analyze ESPN game data and make predictions about the outcomes of games. It utilizes an LLM to perform sentiment analysis on NBA media to influence its predictions.
There are many sports betting apps on the market right now, all with different predictions and analyses of the same games. We aim to utilize machine learning and sentiment analysis to make our own predictions. Users of our app will then be able to identify which bets are potentially profitable and which should be avoided.
We are utilizing Angular and TypeScript in the frontend for the user interface. Here, users can see past, live, and future games, place/view simulated bets, and login/logout.
Our database is Postgres and our backend is Python. We will pull in data from ESPN into our database and use machine learning models to produce an evaluation of the game. We automate this process using Celery, which will allow us to display live game information as updates happen. and retrain our models daily.
We are using Docker in our CI/CD pipeline to deploy our code modularly.