A Non-Linear Approach to Predict the Salary of NBA Athletes using Machine Learning Technique
- Title
- A Non-Linear Approach to Predict the Salary of NBA Athletes using Machine Learning Technique
- Creator
- Jain A.; Jain S.; Pancinovia N.M.; George J.P.
- Description
- Every sportsman traded/drafted receives monetary compensation in accordance with their contract. In this study, we propose a nonlinear approach based on performance and other aspects to determine the salary of a basketball player. We estimate the salary based on four regressive models. Whilst predicting we also Figure out the important features impacting the salary. Comparatively speaking, random forest outperformed other algorithms. Furthermore, we consider that our findings might benefit discussions between basketball teams and players. This model can also help set a benchmark for salary expectations by the players in accordance. 2022 IEEE.
- Source
- 2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Non-Linear Model; Random Forest; Regression machine learning; Salary prediction; Sports analytics; XGBoost
- Coverage
- Jain A., CHRIST (Demeed to Be University), India; Jain S., CHRIST (Demeed to Be University), India; Pancinovia N.M., CHRIST (Demeed to Be University), India; George J.P., CHRIST (Demeed to Be University), India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166545361-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Jain A.; Jain S.; Pancinovia N.M.; George J.P., “A Non-Linear Approach to Predict the Salary of NBA Athletes using Machine Learning Technique,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/20145.