Certified
Self-directed project
Machine Learning for Automotive
AI / ML · September 2026
84/ 100
A ProoV case study · educational project, not employment
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 against Random Forest using R², MAE, and RMSE. The work culminated in a clear CPO pricing recommendation grounded in model performance and feature importance.
Graded against
- Exploratory Data Analysis20%
- Feature Engineering25%
- Model Building & Evaluation35%
- Business Communication20%
Passed · pass mark 60/100
What stood out6
- Compared Linear Regression and Random Forest with R², MAE, and RMSE and clearly identified the stronger model
- Interpreted the Random Forest feature drivers as MPG, car age, and engine size, tying them back to pricing behavior
- Wrote a business recommendation for the CPO team that connects model error to operational pricing decisions
- Leakage-Free Feature Engineering
- Honest Model Evaluation
- Business Translation of Model Results
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 172 words
- VW Sprint1 Complete56 characters
- VW Sprint2 Complete61 characters
- VW Sprint3 Complete59 characters
- VW Business Insights1.5k characters
- Jupyterlite Code2 lines
- VW Sprint1 Complete1 word
- VW Sprint2 Complete1 word
- VW Sprint3 Complete1 word
- VW Business Insights168 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.