Muhammad Huzaifa Aftab completed Machine Learning for Automotive

Built an end-to-end used-car pricing analysis for VW and Audi listings: combined the datasets, explored price and mileage patterns, engineered predictive features, and compared Linear Regression with Random Forest using R² and MAE. The work culminated in a business-facing recommendation to use the Random Forest as a decision-support tool for CPO pricing, with model outputs interpreted through feature importance and operational impact.

• Exploratory Data Analysis Across Segmented Brands
• Leakage-Aware Feature Design
• Model Comparison Using Multiple Regression Metrics
• Business-Oriented Interpretation of Model Results