LightGBM is a gradient boosting framework based on decision trees. It builds trees sequentially, with each new tree correcting the errors of the previous one. The method is particularly suitable for structured, tabular data with many features.

For price predictions on transaction data, LightGBM is well suited for several reasons: it processes large datasets efficiently, captures complex nonlinear relationships between features, and delivers interpretable results. Compared to neural networks, LightGBM requires less training data and computational resources and is less prone to overfitting on structured data.