Certified
Self-directed project
Machine Learning for Automotive
AI / ML · September 2026
68/ 100
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 age-based features, encoded categorical variables, and compared Linear Regression with Random Forest using R², MAE, and RMSE. The final business summary connected model accuracy to pricing decisions and recommended adoption in a CPO pricing pipeline.
Graded against
- Exploratory Data Analysis20%
- Feature Engineering25%
- Model Building & Evaluation35%
- Business Communication20%
Passed · pass mark 60/100
What stood out6
- Created the key predictive features `car_age` and `mileage_per_year` and used them in the modeling workflow.
- Compared Linear Regression against Random Forest using multiple metrics, not just a single score.
- Translated model performance into a pricing recommendation for the CPO team with an explicit financial framing.
- Leakage-Free Feature Engineering
- Comparative Model Evaluation
- Business-Oriented Interpretation
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 106 words
- VW Sprint1 Complete56 characters
- VW Sprint2 Complete61 characters
- VW Sprint3 Complete59 characters
- VW Business Insights850 characters
- Jupyterlite Code2 lines
- VW Sprint1 Complete1 word
- VW Sprint2 Complete1 word
- VW Sprint3 Complete1 word
- VW Business Insights102 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.