Self-directed project
Machine Learning for Automotive
AI / ML · July 2026
A ProoV case study · educational project, not employment
Built an end-to-end used-car pricing analysis for VW and Audi UK listings: combined the datasets, explored price and mileage patterns, engineered predictive features, and compared Linear Regression against Random Forest using R², MAE, and RMSE. The work culminated in a clear CPO recommendation to use the Random Forest model, supported by feature-importance insights and a quantified pricing impact example.
Graded against
- Exploratory Data Analysis20%
- Feature Engineering25%
- Model Building & Evaluation35%
- Business Communication20%
Passed · pass mark 60/100
What stood out6
- Combined VW and Audi listings with a brand column and used brand-aware visualizations to compare market pricing
- Identified the expected negative mileage-price relationship and the Audi premium in the used-car market
- Translated model output into a CPO recommendation, including feature importance and a pricing-risk estimate
- Exploratory Data Analysis
- Feature Engineering for Predictive Modeling
- Model Comparison and Business Interpretation
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 178 words
- VW Sprint1 Complete56 characters
- VW Sprint2 Complete61 characters
- VW Sprint3 Complete59 characters
- VW Business Insights1.6k characters
- Jupyterlite Code2 lines
- VW Sprint1 Complete1 word
- VW Sprint2 Complete1 word
- VW Sprint3 Complete1 word
- VW Business Insights174 words
- Jupyterlite Code1 word
Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.