E-commerce Sales Forecasting Using Machine Learning Algorithm

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Authors
Daulat Desale, Indrayani
Issue Date
2024
Degree
MSc in Business Analytics
Publisher
Dublin Business School
Rights
Abstract
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.