NBA Player Comprehensive Score Prediction based on Linear Regression and Random Forest

Abstract

This paper introduces machine learning models for constructing a comprehensive quality prediction model to forecast National Basketball Association (NBA) players' specific scores. The objective is to facilitate the analysis, guidance, and evaluation of players' relevant value. Initially, data processing involves renaming the dataset and dividing it into an 80% training set and a 20% dataset through preprocessing, simultaneously addressing missing row. Subsequent steps include visualizing the data and conducting correlation analysis by group, producing a correlation heatmap to mitigate multicollinearity issues. Based on the visualization chart's summary, a tentative ability map of players across different positions is delineated, covering rebounds, assists, and other aspects. Employing random forest and linear regression methods, NBA player data is utilized to train the model, followed by comparison of different models' performances and analysis of their respective strengths. Histograms and linear graphs for the linear regression and random forest models are derived, with random forest exhibiting superior fitting to the data, indicating more accurate predictions compared to linear regression. For future projects, the aim is to employ a diverse range of models for comprehensive data analysis and utilize various evaluation methods for detailed assessments of the models.