Certified
Self-directed project
Machine Learning for Automotive
AI / ML · October 2026
78/ 100
A ProoV case study · educational project, not employment
Built an end-to-end used-car pricing workflow for VW and Audi listings in the UK market. Combined and explored the datasets, engineered predictive features such as car age and mileage per year, compared Linear Regression against Random Forest with R²/MAE/RMSE, and 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
- Combined VW and Audi listings correctly with a brand column and verified the merged dataset size
- Used the required evaluation metrics and clearly quantified the Random Forest vs. Linear Regression improvement
- Identified the most important pricing drivers as mileage, car age, and engine size and translated that into a CPO recommendation
- Leakage-Free Data Preparation
- Comparative Model Evaluation
- Business-Oriented Interpretation
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 167 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 Insights163 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.