In-depth Machine Learning Analysis of Morocco's Used Car Market: From Data Mining to Price Prediction

This article addresses the issues of information asymmetry and opaque pricing in Morocco's used car market, using machine learning and data mining techniques to build a vehicle analysis and price prediction system. Core methods include data preprocessing, Principal Component Analysis (PCA), fuzzy clustering, random forest classification, and regression modeling, providing a data-driven solution for used car valuation.

The used car market has long faced issues such as information asymmetry and lack of scientific pricing. This project focuses on the Moroccan market, aiming to solve these pain points through machine learning technology, which has both academic value and provides actionable insights for market participants.

The MUCars-2024 dataset is used, containing over 100,000 Moroccan used car listings. Key attributes include: basic vehicle information (brand, model, year), technical parameters (mileage, transmission type, fiscal power, fuel type), and market information (region, industry classification, selling price), providing a solid foundation for model training.

1. Data preprocessing: Handle missing values, outliers, standardize formats, and create derived features;
2. Exploratory Data Analysis (EDA): Visually understand data distribution and variable relationships;
3. PCA dimensionality reduction: The first two principal components explain 64.65% of total variance;
4. Fuzzy clustering: Identify three types of vehicle profiles (old high-usage type, medium general type, new high-end type);
5. Classification modeling: Random forest classifies vehicle conditions with an accuracy of 70% and AUC of 0.757;
6. Price prediction: Random forest regression is optimal with an R² of approximately 0.82, and key factors include year, mileage, configuration, etc.

• Pricing mechanism: Mainly influenced by year, mileage, and brand positioning, which aligns with global market rules;
• Market segmentation: There is obvious stratification (low-end old, mid-end practical, high-end new);
• Model performance: Random forest for classification tasks has an accuracy of 70%/AUC 0.757, and regression tasks have an R² of 0.82, effectively supporting pricing decisions.

• Consumers: Buyers can estimate reasonable prices to avoid overpaying, and sellers can refer to pricing to improve transaction efficiency;
• Dealers: Batch evaluate inventory and optimize pricing strategies;
• Academia: Provide methodological references for data analysis in traditional industries.

Limitations: The dataset does not include warehouse-related data and needs to be downloaded independently, model performance needs improvement, and market fluctuations and seasonality are not considered; Future: Introduce deep learning for comparison, add time series analysis, integrate macro data, and develop interactive web applications.