First run data_collection.py, then grid_search.py, and last model.py

Each csv needed is created by the previous python file.

This project uses deep learning techniques to predict whether the Milwaukee Bucks will win or lose a game based on statistics from their roster. Historical data such as players' average points per game, rebounds per game, and assists per game are used to build the model.

The data used in this project is collected from publicly available sources such as the official NBA website and third-party websites. The collected data is preprocessed through tasks such as data cleaning, handling missing values, scaling the data, encoding categorical variables, and feature engineering.

The deep learning model is built using forward neural networks (FNNs) and Python programming language. Libraries such as Tensorflow, Keras, and Pandas are used to build and train the model. Techniques such as hyperparameter tuning and regularization are used to optimize the model's performance.

This project demonstrates the use of deep learning techniques in predicting basketball game outcomes. The model built can be used to make predictions on future games and can provide valuable insights for coaches and analysts.