Self-directed project
Machine Learning for Automotive
AI / ML · September 2026
A ProoV case study · educational project, not employment
Built an end-to-end used-car pricing workflow 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 translated the results into a Certified Pre-Owned pricing recommendation. The work shows practical data analysis, model evaluation, and business-facing communication.
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 a naive mileage-only baseline and then moved to engineered features and model comparison, showing an understanding of why richer predictors matter.
- Identified the main valuation drivers as registration year, engine size, and mileage, and translated model output into a CPO pricing recommendation.
- Exploratory Data Analysis
- Feature Engineering
- Model Comparison
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 147 words
- VW Sprint1 Complete56 characters
- VW Sprint2 Complete61 characters
- VW Sprint3 Complete59 characters
- VW Business Insights1.3k characters
- Jupyterlite Code2 lines
- VW Sprint1 Complete1 word
- VW Sprint2 Complete1 word
- VW Sprint3 Complete1 word
- VW Business Insights143 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.