This project focuses on using a decision tree algorithm to analyze the salaries of NBA players based on their performance data. The main goal is to predict player salaries using key features such as their age, average minutes played, and win shares. By analyzing these features, we can understand how player performance impacts their earnings.
The project involves building and optimizing a decision tree model. First, we tested different values for the minimum split parameter to find the best balance between overfitting and underfitting. Then, we identified the highest and lowest salary nodes in the tree and analyzed the features that influenced them. Finally, we used the model to predict salaries for selected player data points, demonstrating how decision trees can provide meaningful insights into salary structures.
