Certified
Self-directed project
Machine Learning for Automotive
AI / ML · October 2026
72/ 100
A ProoV case study · educational project, not employment
Built a used-car pricing analysis for VW and Audi listings by combining the datasets, exploring price and mileage distributions, checking correlations, and comparing brand-level pricing patterns. The work shows practical feature awareness and business judgment, with a clear recommendation to use model-assisted pricing, review large prediction gaps, include model name, and retrain regularly.
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.
- Identified skew, outliers, and likely data quality issues such as engineSize = 0 and extreme mpg values.
- Connected the visual findings to business meaning by noting Audi's higher median price may be driven by model mix rather than brand alone.
- Translated model output into a CPO-style recommendation about pricing review thresholds, adding model name, and monthly retraining.
- Exploratory Data Analysis
- Feature Engineering Awareness
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 182 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 Insights178 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.