Certified
Self-directed project
Machine Learning for Automotive
AI / ML · June 2026
68/ 100
A ProoV case study · educational project, not employment
A verified, self-directed industry project — completed and passed a real industry rubric at 68/100 on ProoV.
Graded against
- Exploratory Data Analysis20%
- Feature Engineering25%
- Model Building & Evaluation35%
- Business Communication20%
Passed · pass mark 60/100
What stood out6
- Correctly combined VW and Audi datasets with a brand column and performed multiple EDA views, including distributions, correlations, scatter plots, and brand boxplots.
- Identified meaningful predictive features such as car age and mileage per year, and connected them to resale value.
- Compared Linear Regression and Random Forest conceptually and translated model performance into a business impact estimate for the CPO team.
- Provided an actionable recommendation to use Random Forest as a decision-support tool with human review for unusual cases.
- Exploratory Data Analysis
- Feature Engineering
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 184 words
- VW Sprint1 Complete56 characters
- VW Sprint2 Complete61 characters
- VW Sprint3 Complete59 characters
- VW Business Insights1.6k characters
- Jupyterlite Code2 lines
- VW Sprint1 Complete1 word
- VW Sprint2 Complete1 word
- VW Sprint3 Complete1 word
- VW Business Insights180 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.