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 workflow for VW and Audi listings: combined the datasets, explored price and mileage patterns, engineered predictive features, and trained both Linear Regression and Random Forest models. The work culminated in a business summary that translated model results into a practical recommendation for a Certified Pre-Owned purchasing dashboard.
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 with a brand column and used the merged dataset consistently in analysis
- Added the key predictive features and cleaned obvious data errors before modeling
- Compared Linear Regression against Random Forest using R², MAE, and RMSE rather than relying on a single metric
- Wrote an executive summary that connects model performance to a concrete CPO purchasing recommendation
- Leakage-Free Feature Engineering
- Honest Model Evaluation
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 166 words
- VW Sprint1 Complete56 characters
- VW Sprint2 Complete61 characters
- VW Sprint3 Complete59 characters
- VW Business Insights1.4k characters
- Jupyterlite Code2 lines
- VW Sprint1 Complete1 word
- VW Sprint2 Complete1 word
- VW Sprint3 Complete1 word
- VW Business Insights162 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.