Certified
Self-directed project
Machine Learning for Automotive
AI / ML · October 2026
72/ 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 distributions, examined correlations and brand differences, and compared Linear Regression against Random Forest using R², MAE, and RMSE. The work also translated model output into a practical CPO pricing recommendation with a human-in-the-loop review threshold.
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 into a single analysis table with a brand indicator
- Used the required regression metrics and clearly compared Linear Regression against Random Forest
- Connected model performance to pricing risk and a concrete CPO workflow recommendation
- Exploratory Data Analysis
- Leakage-Free Feature Framing
- Model Comparison with Business Metrics
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 Insights1.7k 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.