Certified
Self-directed project
Machine Learning for Automotive
AI / ML · July 2026
78/ 100
Built an end-to-end used-car pricing analysis for VW and Audi listings: combined the datasets, explored price and mileage patterns, and compared Linear Regression with Random Forest using holdout evaluation. The work also translated model performance into a business-facing pricing exposure estimate and recommended a data-driven pricing workflow for the CPO 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 table with a brand indicator and used it consistently across EDA.
- Used multiple EDA views: histograms, correlation heatmap, scatter plots, and a brand boxplot to compare market behavior.
- Identified the strongest business drivers in the model output and translated MAE into a pricing exposure estimate for the CPO team.
- Compared Linear Regression against Random Forest and reported both R²-style accuracy and MAE differences in the business summary.
- Exploratory Data Analysis
- Leakage-Free Feature Preparation
The work I submitted5 tasks
VW Sprint1 Complete
My solution
{"completed":true,"notebookName":"vw_sprint1_eda.ipynb"}VW Sprint2 Complete
My solution
{"completed":true,"notebookName":"vw_sprint2_features.ipynb"}VW Sprint3 Complete
My solution
{"completed":true,"notebookName":"vw_sprint3_models.ipynb"}VW Business Insights
My solution
{"text":"Our Random Forest model predicts used car resale prices with 95.0% accuracy compared to the Linear Regression model which predicts with 83.3% accuracy. Our Random Forest model also reduces the MAE from £2,622 to £1,438.\nThe 3 most important features that my Random Forest model identified are car's age, mileage and engine size. The newer car with low mileage and having large engine size, retain higher resale value because of less damage with better performance and higher specifications than the older car with high mileage and small engine.\nFrom a business perspective, if my model has an MAE of £1,500, and VW sells 100,000 used cars annually, then the total pricing exposure could be around £150 millions, if every vehicle were mispriced by the average amount. Even though having a 95% accuracy but because of that 5% of uncertainty still costs the business real money.\nI recommend that the CPO (Certified Pre-Owned) team must adopt my Random Forest Model as their primary tool to predict the resale prices of used cars. Using the data-driven alongside expert review will improve the overall efficiency and will reduce the manual pricing errors. It will also optimize the inventory margins.","charCount":1198,"selfChecks":[{"label":"Model comparison","checked":true},{"label":"Top features identified","checked":true},{"label":"Financial Impact","checked":true},{"label":"Clear recommendation","checked":false}]}Jupyterlite Code
My solution
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