Certified
Self-directed project
Machine Learning for Automotive
AI / ML · August 2026
88/ 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. The work combined exploratory analysis, data cleaning, feature engineering, and model comparison, then translated the results into a CPO pricing recommendation. It showed strong practical judgment by using Random Forest as the preferred model and explaining the business impact in terms leadership could act on.
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 and used brand-aware EDA to surface the premium gap between the two marques.
- Identified and removed clearly invalid rows such as zero-engine-size and extreme price outliers before modeling.
- Compared Linear Regression and Random Forest using R², MAE, and RMSE, and reported a substantial improvement for the tree model.
- Connected model output to CPO decision-making by estimating pricing exposure and recommending a practical deployment path.
- Leakage-Free Feature Engineering
- Comparative Model Evaluation
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 189 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 Insights185 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.