In order to attract new consumers, retain existing ones, and increase revenue, online businesses should offer a diverse array of pricing options. The objective of this investigation is to create an intelligent model for predicting price fluctuations by employing machine learning techniques, specifically Long Short-Term Memory and Recurrent Neural Networks. The most effective method of determining the current price of a product is to analyze its past prices, market trends, consumer behavior, and demand patterns. Stochastic gradient descent, or SGD, is a technique that employs gradients to improve the performance of a model and address its deficiencies. The research demonstrated that pricing models based on LSTMs are highly effective in identifying temporal correlations and adapting to evolving market conditions. Traditional machine learning techniques are outperformed by these models. The distinctive approach of this method has the potential to increase consumer confidence, profits, and sales. The findings underscore the critical role of automated pricing systems that are propelled by machine learning in enabling customers to make informed decisions in the highly competitive online marketplaces.
