Zertifiziert
Selbstgesteuertes Projekt
Machine Learning for Automotive
KI / ML · Juli 2026
84/ 100
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 with Random Forest using R², MAE, and RMSE. The work also included a business-facing assessment of pricing risk and a recommendation to keep premium-brand pricing under human review when model outputs fall outside expected ranges.
Bewertet nach
- Exploratory Data Analysis20%
- Feature Engineering25%
- Model Building & Evaluation35%
- Business Communication20%
Bestanden · Bestehensgrenze 60/100
Was herausstach6
- Built a clear EDA flow across both brands, including price/mileage distributions, correlation analysis, scatter plots, and a brand-level boxplot.
- Compared Random Forest against Linear Regression using business-relevant metrics and correctly interpreted the stronger performance of the tree model.
- Translated model error into portfolio-level pricing exposure and used a concrete Golf vs Audi example to support a human-review recommendation.
- Exploratory Data Analysis
- Leakage-Aware Feature Engineering
- Model Comparison and Interpretation
Meine eingereichte Arbeit5 Aufgaben
VW Sprint1 Complete
Meine Lösung
{"completed":true,"notebookName":"vw_sprint1_eda.ipynb"}VW Sprint2 Complete
Meine Lösung
{"completed":true,"notebookName":"vw_sprint2_features.ipynb"}VW Sprint3 Complete
Meine Lösung
{"completed":true,"notebookName":"vw_sprint3_models.ipynb"}VW Business Insights
Meine Lösung
{"text":"1. Random Forest has performed better as compare to Linear Regression. As Random Forest got 0.9504 R2-score and £1438 MAE while Linear Regression got 0.8328 and 2622 respectively. Which mean Random Forest explains around 12 percentage points more variance.\n\n2. mpg, car_age, enginesize are 3 important pricing features.\n\n3. If model give average error of £1500 and VW sells 100000 used car a years then it will make a total exposure of £150000000. Now this £150M isn't company loss as it tells the aggregate size of pricing uncertainty across the whole portfolio in a year. So £150M is best framed as the scale of risk this model still carries, even though it's a strong model. It is not a guaranteed loss figure. And compare to the old manual process where just looking by eyes or putting a checklist are typically less consistent and less accurate than the model which are trained on thousand examples. Even £150M is smaller than what old manual process carried as Random Forest is 45% better than basic linear approach, which is far better than the old manual process.\n\n4. When I tested the model on a Golf and an Audi. The Golf landed right in the expected range ( £12-14k). The Audi's prediction £26,465 came in above the expected range of £18-22k. This evidence-based finding the model looks more trustworthy on mass market cars than on premium brands. As I recommend don't fully automate pricing for premium brands, route through human review.","charCount":1450,"selfChecks":[{"label":"Model comparison","checked":false},{"label":"Top features identified","checked":false},{"label":"Financial Impact","checked":false},{"label":"Clear recommendation","checked":false}]}Jupyterlite Code
Meine Lösung
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