E-commerce Sales Forecasting Using Machine Learning Algorithm

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Daulat Desale, Indrayani
Issue Date
MSc in Business Analytics
Dublin Business School
Businesses looking to maximise inventory, marketing tactics, and overall operational efficiency must consider e-commerce sales forecasts when making strategic decisions. The goal of this project is to determine the best method for predicting e-commerce sales by developing, assessing, and contrasting three time series machine learning models: ARIMA, Facebook (FB) Prophet and LSTM. The goals of the study are to prepare the dataset, create the model, tune the hyperparameters, and evaluate the performance. It explains how complicated e-commerce sales trends are and highlights the need for improved models or tactics to identify subtle patterns. Out of all the 3 models Facebook (FB) Prophet outperformed ARIMA and LSTM with decent evaluation matrix score like (RMSE): 273.79, (MSE): 74965.26, (MAE): 221.24. It also identifying intricate patterns and offering insightful analysis of e-commerce sales data. However the FB prophet also fails to give accurate e-commerce sales predictions like the ARIMA and LSTM models.