Certified
Self-directed project
Machine Learning for Automotive
AI / ML · September 2026
91/ 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, explored price and mileage patterns, engineered predictive features, compared Linear Regression against a tuned Random Forest, and translated the results into a practical CPO pricing guardrail backed by model metrics and feature insights.
Graded against
- Exploratory Data Analysis20%
- Feature Engineering25%
- Model Building & Evaluation35%
- Business Communication20%
Passed · pass mark 60/100
What stood out6
- Built a clear VW vs Audi comparison with brand-level boxplots and scatter plots that surfaced the premium gap.
- Used multiple evaluation metrics in the business summary, including R² and MAE, to quantify the improvement from Linear Regression to Random Forest.
- Identified the most influential drivers of price in the market narrative, including MPG, car age, and engine size.
- Turned model output into a concrete operational recommendation: a £1,500 deviation threshold for management review.
- Exploratory Data Analysis
- Leakage-Aware Feature Engineering
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 184 words
- VW Sprint1 Complete56 characters
- VW Sprint2 Complete61 characters
- VW Sprint3 Complete59 characters
- VW Business Insights1.5k characters
- Jupyterlite Code2 lines
- VW Sprint1 Complete1 word
- VW Sprint2 Complete1 word
- VW Sprint3 Complete1 word
- VW Business Insights180 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.