Certified
Self-directed project
Machine Learning for Automotive
AI / ML · September 2026
78/ 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 against Random Forest using regression metrics. The work culminated in a business-facing recommendation that tied model accuracy to pricing risk and operational use for a Certified Pre-Owned team.
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 into a single analysis frame with a brand indicator and used it consistently in comparisons.
- Built multiple EDA views that directly support the pricing problem, including price/mileage distributions, correlation analysis, scatter plots, and a brand-level boxplot.
- Translated model output into business terms by quantifying pricing error and recommending a deployment approach for the CPO team.
- Exploratory Data Analysis Across Brands
- Leakage-Aware Feature Framing
- Model Comparison and Business Translation
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 191 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 Insights187 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.