Fizza Javaid completed Machine Learning for Automotive

Built an end-to-end used-car pricing workflow for VW and Audi listings: combined the datasets, explored price and mileage patterns, engineered predictive features, and trained both Linear Regression and Random Forest models. The final recommendation favored Random Forest as the pricing baseline, supported by a clear metric comparison and feature-importance interpretation for a simulated CPO team.

• Leakage-Free Feature Engineering
• Model Comparison with Business Metrics
• Data Exploration Across Segments