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 listings in the UK market. Combined the datasets, engineered predictive features such as car age and mileage-per-year, trained and compared Linear Regression and Random Forest models, and recommended Random Forest for deployment based on stronger R², MAE, and RMSE performance. Also connected the results to a practical Certified Pre-Owned pricing workflow.
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 frame with a brand indicator and explored cross-brand pricing differences.
- Used derived predictors such as car age and mileage-per-year to strengthen the pricing signal before modeling.
- Compared Linear Regression against Random Forest with R², MAE, and RMSE, then selected the stronger model for the CPO use case.
- Translated model performance into a clear pricing recommendation for the Certified Pre-Owned team.
- Leakage-Free Feature Engineering
- Honest Model Evaluation
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 Insights2.0k 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.