Machine Learning-driven Dynamic Pricing Strategies in E-Commerce

Dynamic pricing, the practice of adjusting prices in real-time based on various factors, has gained significant attention in the e-commerce industry. This paper presents a study on dynamic pricing using machine learning techniques to develop an accurate and effective pricing model. The study utilizes historical transaction data from an e-commerce platform and applies feature engineering and model selection to identify the most suitable machine learning algorithm. Gradient Boosting Machines (GBM) emerges as the primary model due to its ability to capture complex relationships and provide accurate predictions. The GBM model is trained and tuned using hyperparameter optimization techniques, and its performance is evaluated using Mean Squared Error (MSE) and R-squared (R2) score. The results demonstrate the superior performance of the GBM model compared to other algorithms, achieving a low MSE of 0.012 and a high R2 score of 0.92 on the validation set. The findings highlight the potential of machine learning in optimizing revenue and enhancing customer satisfaction through personalized pricing strategies.