Sales Prediction in E-Commerce Platforms Using Machine Learning

The rapidly evolving e-commerce platforms have reshaped consumer behavior, creating an imperative for accurate sales forecasting models. This paper delves into predictive analytics, using machine learning, focusing on utilizing Long Short-Term Memory (LSTM) networks for sales prediction within the e-commerce domain. Leveraging a comprehensive dataset sourced from Taobao, a prominent e-commerce platform, this study employs LSTM-based models to forecast sales trends, considering factors such as user interactions, browsing patterns, and purchase behavior. The investigation encompasses preprocessing techniques to prepare the dataset for LSTM model training, emphasizing sequential dependencies and temporal dynamics inherent in e-commerce data. Through accurate evaluations using standard metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), the efficacy of LSTM models in predicting sales patterns is scrutinized. The paper highlights the potential implications of accurate sales forecasting in optimizing inventory management, marketing strategies, and decision-making within the e-commerce landscape. This study contributes to the growing knowledge of leveraging LSTM networks for precise sales prediction in e-commerce, providing insights for future advancements in predictive analytics within this dynamic domain.