Certified
Self-directed project
Machine Learning for Automotive
AI / ML · July 2026
84/ 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, cleaned invalid records, engineered predictive features, and compared Linear Regression against Random Forest using R², MAE, and RMSE. The work culminated in a business recommendation for a Certified Pre-Owned team, grounded in model performance and feature importance.
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 and used brand-aware visualizations to compare market behavior.
- Identified and removed clearly invalid rows such as zero engine-size and extreme price outliers before modeling.
- Built and compared both Linear Regression and Random Forest models, then communicated the performance gap using R², MAE, and RMSE.
- Translated model output into a CPO-oriented recommendation with an explicit pricing-risk narrative.
- Exploratory Data Analysis
- Leakage-Free Feature Preparation
The work I submitted10 tasks
5 tasks · 6 lines · 5 written answers · 164 words
- VW Sprint1 Complete56 characters
- VW Sprint2 Complete61 characters
- VW Sprint3 Complete59 characters
- VW Business Insights1.3k characters
- Jupyterlite Code2 lines
- VW Sprint1 Complete1 word
- VW Sprint2 Complete1 word
- VW Sprint3 Complete1 word
- VW Business Insights160 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.