Forecasting the NBA's Most Valuable Player: A Regression Analysis Approach

Basketball is immensely popular in the United States, with the National Basketball Association (NBA) recognized as the leading professional league worldwide. Each season, the NBA awards the Most Valuable Player (MVP) title to the player considered the best based on performance statistics and a fair voting process. Predicting the MVP often sparks debates among NBA fans, with various media outlets offering their forecasts. This study aims to accurately predict the MVP by analyzing statistical data from NBA players over a specific period. We employ three different regression algorithms—Decision Tree, Linear Regression, and Support Vector Regression—to compare their effectiveness in MVP prediction. The research findings will highlight the potential MVP winner based on their performance statistics, presented in a simplified dashboard format for easy comprehension.