In this personal project, an in-depth analysis was conducted to identify the key NBA player statistics that have the most significant impact on players' salaries. Through R, advanced data transformation techniques and rigorous model verification methods were employed to arrive at conclusive findings. For further insights, please refer to the attached .pdf file, which provides a detailed report on the project. Additionally, you can access the .Rmd file to explore the R code used for the analysis, gaining a deeper understanding of the employed methodology.
- In-depth analysis to identify the key NBA player statistics impacting salaries.
- Used advanced data transformation techniques and rigorous model verification methods in R.
- Detailed report in the attached .pdf file provides comprehensive findings and insights.
- Access the .Rmd file to explore the R code used in the analysis.
.csv: Contains the datasets used for the analysis..Rmd: Includes the R script for the data analysis..pdf: Provides detailed documentation explaining the project methodology and findings.
To reproduce the analysis and explore the findings:
- Clone this repository:
git clone https://github.com/umerabbasi-git/Predicting-Player-Salaries.git - Install R and any necessary dependencies.
- Open the R script and execute it to run the data analysis.
- Refer to the documentation for a comprehensive explanation of the project methodology and findings.
Feel free to reach out if you have any questions or feedback regarding the project.