Certified
Self-directed project
Machine Learning for Automotive
AI / ML · September 2026
88/ 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, trained and compared Linear Regression and Random Forest models, and selected the stronger model based on R², MAE, and RMSE. Also translated the results into a practical pricing recommendation for a Certified Pre-Owned team.
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 using R², MAE, and RMSE with clear numeric results
- Identified the strongest pricing drivers as mpg, car age, and engine size
- Translated model error into a portfolio-level mispricing estimate for the CPO team
- Leakage-Free Feature Engineering
- Honest Model Evaluation
- Business Translation of Model Results
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 164 words
- VW Sprint1 Complete56 characters
- VW Sprint2 Complete61 characters
- VW Sprint3 Complete59 characters
- VW Business Insights1.2k characters
- Jupyterlite Code2 lines
- VW Sprint1 Complete1 word
- VW Sprint2 Complete1 word
- VW Sprint3 Complete1 word
- VW Business Insights160 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.