NBA Game Outcome Prediction

Description

In this project, I will focus on predicting the outcome of NBA games using machine learning models based on key game statistics. I will work with a dataset that includes information such as points scored, field goal percentages, assists, rebounds, and whether the home team won or lost. I will begin by exploring and preprocessing the data, handling any missing values, creating new features like the point difference, and converting categorical variables into dummy variables for modeling. I will perform exploratory data analysis to understand the relationships between various statistics and game outcomes, using visualizations like histograms and heatmaps. I will then train a Random Forest model to predict whether the home team will win, using features like points, assists, and rebounds, and evaluate the model’s performance with metrics such as accuracy and precision. I expect the results to be promising, and I will conclude by discussing how certain statistics, like field goal percentage and point difference, are strong predictors of game outcomes, while also suggesting further improvements for future work.

Project Report 👈