Skip to content
/ NBA-Performance-Analysis-Model Public

A Comprehensive Analysis of NBA Team Performance through Logistic and Linear Regression and Random Forest Classifier for Predictive Modeling

0 stars 1 fork Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

Louis-Miguel/NBA-Performance-Analysis-Model

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
README.md
README.md
 
 
algorithm_and_results.ipynb
algorithm_and_results.ipynb
 
 

Repository files navigation

Comprehensive Analysis of NBA Team Performance through Logistic and Linear Regression and Random Forest Classifier for Predictive Modeling

This is a course requirement for CS 128 Software Engineering Courses of the Department of Computer Science, College of Engineering, University of the Philippines, Diliman under the guidance of Prof. John Justine Villar for A.Y. 2023-2024
  • Fermiza, Louis
  • Guiao, Ezra
  • Gacho, Loridge
  • Ducay, James
  • Garais, Zandrew

Background

Basketball has become a widely embraced global sport with fans spanning various age groups. This surge in popularity has inspired our statistical examination of basketball teams to assess their performance. The National Basketball Association (NBA), a professional basketball league in North America comprising 30 teams (NBA Staff, 2019), remains a significant contributor to the sport's global appeal, as indicated by Statista Research Department (2021). Perhaps the most obvious question in basketball analytics is which NBA players go above and beyond to assist their teams in winning games. Furthermore, it is also desirable to recognize which specific statistic or attribute has the biggest impact on winning games.

In the larger field of sports analytics, this analysis model attempts to improve our comprehension of NBA team dynamics and make a positive impact on statistical modeling and analytical developments. The study aims to provide useful tools for both fans and team owners by addressing fundamental problems regarding forecasting NBA results.

About

A Comprehensive Analysis of NBA Team Performance through Logistic and Linear Regression and Random Forest Classifier for Predictive Modeling

Resources

Readme
Activity

Stars

0 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

No releases published

Packages

No packages published

Languages