Suraj Panda completed Machine Learning for Automotive

Built an end-to-end used-car pricing analysis for VW and Audi listings: combined the datasets, explored brand and feature relationships, and trained both Linear Regression and Random Forest models. The work compared models with R² and MAE, identified mpg, car age, and engine size as the main pricing drivers, and delivered a clear recommendation for a CPO pricing decision-support workflow.

• Leakage-Free Feature Engineering
• Comparative Model Evaluation
• Business-Oriented Insight Synthesis