LightGBM-Based Models for Weighted Price Prediction

Abstract

Accurate price prediction is a cornerstone in various fields, such as stock market analysis, e-commerce, and real estate. Traditional machine learning methods often fail to efficiently handle large datasets with high dimensionality and complex feature interactions. LightGBM, a gradient-boosting framework, emerges as a robust solution due to its efficiency and scalability. This paper explores the application of LightGBM for weighted price prediction, leveraging its ability to handle large-scale data and imbalanced distributions. Experimental results demonstrate its superiority in terms of predictive accuracy, computational efficiency, and flexibility in feature engineering. The study further discusses the integration of domain-specific knowledge and optimization techniques to enhance the model’s performance. The findings suggest that LightGBM can serve as a valuable tool for real-time price prediction in dynamic environments.