Certified
Self-directed project
Machine Learning for Automotive
AI / ML · September 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: combined the datasets, explored price and mileage patterns, engineered predictive features, and compared Linear Regression with Random Forest using R² and MAE. The work culminated in a practical CPO recommendation to use the Random Forest model, backed by feature-importance insights and a business impact estimate.
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 label and used that structure consistently across EDA and modeling.
- Identified the strongest price relationships and supported them with concrete observations, including the Audi price premium.
- Compared Linear Regression against Random Forest using multiple metrics and selected the stronger model for the CPO use case.
- Translated model performance into a business impact statement and a clear recommendation for the pricing team.
- Exploratory Data Analysis
- Leakage-Free Feature Engineering
The work I submitted2 tasks
1 task · 2 lines · 1 written answer · 1 word
- Jupyterlite Code2 lines
- 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.