Leveraging machine learning to predict e-commerce shopping behaviour and enhance recommendations
Authors
Ahmed, Sheikh Fuad
Issue Date
2024
Degree
MSc in Business Analytics
Publisher
Dublin Business School
Rights holder
Rights
Items in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.
Abstract
This study explored how machine learning techniques could predict e-commerce shopping behaviour and enhance product recommendations. The research analysed data from user interactions, purchase histories, and sentiment analysis to develop effective models. It involved thorough data preparation, including handling missing values, processing text with TFIDF, and applying SMOTE to balance the data. Various models were tested such as Logistic Regression, AdaBoost, Random Forest, Naive Bayes, XGBoost, and Linear Support Vector Machine. The results indicated that Logistic Regression and AdaBoost were most effective for predicting shopping behaviour while Logistic Regression and Linear Support Vector Classifier excelled in sentiment analysis. These models achieved high accuracy, precision, and recall, demonstrating their practicality for real-world e-commerce applications. Implementing these models allowed e-commerce platforms to offer personalized recommendations, enhance customer satisfaction and increase sales. This study demonstrated the significant potential of machine learning to improve e-commerce strategies and enrich the overall shopping experience.