Used Vehicle Pricing & Valuation Model
Regression case study using feature engineering and tree ensembles on cross-country car listings.
Built a regression model to predict used car prices across 8 countries using 100K listings. Cleaned outliers, engineered features, and tuned tree-based models (Random Forest, Gradient Boosting) to reach R² = 0.867 and MAE ≈ €2,660.
Context
Problem and Context
Built a regression model to predict used car prices across 8 countries using 100K listings. Cleaned outliers, engineered features, and tuned tree-based models (Random Forest, Gradient Boosting) to reach R² = 0.867 and MAE ≈ €2,660.
Approach
Approach and Architecture
Regression models predicting used car prices with engineered features.
Implementation
Implementation Details
Results
Results and Tradeoffs
This project is presented as a concise technical overview rather than a full-length narrative case study.
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