In-depth Analysis of the European Automotive Market: A Machine Learning Price Prediction Study Based on AutoScout24 Data

This article conducts a car price prediction study using machine learning technology based on massive data from AutoScout24, a leading European automotive trading platform. The core goal is to analyze the characteristics of the European automotive market through data science methods, build accurate prediction models, and provide data support for decision-making in the automotive industry. The study covers dataset value mining, market structure analysis, feature engineering construction, model selection and evaluation, and finally reveals market laws and proposes business application directions, demonstrating the potential of data science to empower traditional industries.

The AutoScout24 dataset covers multiple countries such as Germany, France, Italy, and the Netherlands. It is large-scale (millions of vehicle records), rich in dimensions (basic features + technical configurations + seller information), and highly authentic (real transaction scenarios), but has quality issues like missing values and outliers that need preprocessing. The European automotive market has distinct characteristics: in terms of brand structure, German luxury brands dominate the high-end segment, Volkswagen holds the mid-end, and French and Italian brands have local loyalty; environmental regulations are driving electrification transformation, with the proportion of diesel vehicles declining; the used car market is mature, with active transactions and diverse price distributions.

Feature engineering is key, including basic features (conversion of brand, mileage, etc.), derived features (vehicle age, mileage-to-age ratio, etc.), market features (number of vehicles for sale, listing time, etc.), and time features (season, holidays, etc.). The model explores multiple algorithms: linear regression as a baseline but struggles to capture non-linearity; decision trees/random forests can learn non-linearity and interactions; gradient boosting trees (XGBoost/LightGBM) have higher accuracy and are currently mainstream; neural networks are less efficient on structured data.

Evaluation uses metrics such as RMSE (intuitive but sensitive), MAE (robust), and R² (proportion of variance explained). Hierarchical evaluation shows differences in model performance across different price ranges, brands, and vehicle ages; residual analysis identifies systematic biases (e.g., underprediction for specific models); feature importance reveals that mileage, vehicle age, and brand are the main influencing factors, which aligns with market laws.

The study reveals multi-dimensional market laws: depreciation curves (luxury brands depreciate quickly in the early stage but retain value well later), brand premium (contribution of intangible value), configuration value (differences in premiums for different configurations), and trend prediction (decline in electric vehicle prices, etc.). Application scenarios include trading platform valuation tools, financial institution risk management, consumer decision support, etc., to improve market transparency and efficiency.

Current limitations: data scope may be limited, missing features (accident/maintenance records), static models struggle to adapt to dynamic markets. Improvement directions: expand multi-country data, integrate external economic indicators/new car information, time series modeling to capture trends, online learning to update models, multi-modal fusion (image/text data), causal inference to distinguish between correlation and causality, etc.