Selbstgesteuertes Projekt
Machine Learning for Automotive
KI / ML · Juli 2026
Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis
Built an end-to-end used-car pricing analysis for VW and Audi listings in the UK market. Combined the datasets, engineered predictive features such as car age and mileage-per-year, trained and compared Linear Regression and Random Forest models, and recommended Random Forest for deployment based on stronger R², MAE, and RMSE performance. Also connected the results to a practical Certified Pre-Owned pricing workflow.
Bewertet nach
- Exploratory Data Analysis20%
- Feature Engineering25%
- Model Building & Evaluation35%
- Business Communication20%
Bestanden · Bestehensgrenze 60/100
Was herausstach6
- Combined VW and Audi listings into a single analysis frame with a brand indicator and explored cross-brand pricing differences.
- Used derived predictors such as car age and mileage-per-year to strengthen the pricing signal before modeling.
- Compared Linear Regression against Random Forest with R², MAE, and RMSE, then selected the stronger model for the CPO use case.
- Translated model performance into a clear pricing recommendation for the Certified Pre-Owned team.
- Leakage-Free Feature Engineering
- Honest Model Evaluation
Meine eingereichte Arbeit10 Aufgaben
5 Aufgaben · 6 Zeilen · 5 Textantworten · 167 Wörter
- VW Sprint1 Complete56 Zeichen
- VW Sprint2 Complete61 Zeichen
- VW Sprint3 Complete59 Zeichen
- VW Business Insights2.0k Zeichen
- Jupyterlite Code2 Zeilen
- VW Sprint1 Complete1 Wort
- VW Sprint2 Complete1 Wort
- VW Sprint3 Complete1 Wort
- VW Business Insights163 Wörter
- Jupyterlite Code1 Wort
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