This project provides a comprehensive analysis and comparison of the efficiency and effectiveness of R and Python programming languages in constructing machine learning (ML) pipelines. The study evaluates various metrics and performance parameters, such as CPU time, memory usage, model accuracy (MSE, MAE, RMSE), and data visualization capabilities. The repository includes code implementations, datasets, and visualizations. It aims to provide valuable insights for practitioners seeking to choose the appropriate language for their ML projects, specifically focusing on the automotive industry and the used car market.
r1shabhpahwa/ML_Pipeline_Comparison
An in-depth analysis comparing the efficiency and effectiveness of R and Python programming languages for constructing machine learning pipelines in the automotive industry, with insights on metrics, model performance, and data visualization.
Jupyter Notebook