Machine Learning Regression Model: Exploring Regression Algorithms for Mercedes-Benz Price Prediction

Abstract

Predicting luxury car prices, such as Mercedes-Benz, remains challenging due to multiple interacting variables, including model, ratings, and market conditions. This study compares six regression algorithms, Linear Regression, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and AdaBoost, to identify the most effective model for Mercedes-Benz price prediction. A Kaggle dataset of 10,432 records was preprocessed through cleaning, removal of missing values (resulting in 10,307 records), One-Hot Encoding for categorical variables, and standardization of numerical features using StandardScaler, then split into 80% training and 20% testing data. Model performance was evaluated using MSE, RMSE, and R². Random Forest achieved the best performance (R² = 0.97; RMSE: $3,917), followed closely by Gradient Boosting (R² = 0.96; RMSE: $4,359) and XGBoost (R² = 0.96; RMSE: $4,305). Linear Regression achieved a similar R² (0.96) but higher errors (RMSE: $4,767), while AdaBoost (R² = 0.95; RMSE: $4,897) and KNN (R² = 0.90; RMSE: $5,657) showed lower performance. These findings confirm that ensemble methods, particularly Random Forest, significantly outperform traditional and distance-based approaches for luxury car price prediction. This study provides a comprehensive comparative framework for automotive pricing analytics, with future research directions including additional features, hyperparameter tuning, and integration of external market factors to further enhance prediction accuracy.