A Hybrid Gradient Boosting Algorithm for Dynamic Pricing using a Custom dataset

E-commerce platforms have experienced significant growth, especially during the COVID-19 pandemic, leading to an influx of sellers and buyers. However, pricing products accurately amidst this surge in online shopping presents a challenge due to various factors like seasonal trends and product specifications. To address this challenge, a novel approach is introduced in this study, combining XGBoost with NGBoost to predict product prices based on reviews and various features. The hybrid models are compared with the base models XGBoost and NGBoost, demonstrating superior performance over these existing algorithms. This offers business owners a competitive edge in pricing their products effectively.