Certified
Self-directed project
Machine Learning for Automotive
AI / ML · July 2026
92/ 100
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, performed EDA, engineered predictive features, trained and compared Linear Regression and Random Forest models, and used the results to recommend an automated CPO pricing pipeline based on the stronger model’s lower error and higher R².
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 distributions, correlations, and brand-level price differences.
- Built and compared two regression models using proper train/test evaluation and reported R² and MAE improvements for Random Forest over Linear Regression.
- Identified the top pricing drivers from the model and translated the error metric into a portfolio-level pricing risk for the CPO business.
- Leakage-Free Feature Engineering
- Comparative Model Evaluation
- Business Translation of ML Results
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 113 words
- VW Sprint1 Complete56 characters
- VW Sprint2 Complete61 characters
- VW Sprint3 Complete59 characters
- VW Business Insights956 characters
- Jupyterlite Code2 lines
- VW Sprint1 Complete1 word
- VW Sprint2 Complete1 word
- VW Sprint3 Complete1 word
- VW Business Insights109 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.