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- W3208261001 abstract "As Machine Learning (ML) algorithms are becoming popular to solve everyday challenges, it is being used to make predictions on data across different industries. To obtain an advantage over the competitive pressures, a service or goods supplying firm needs a strong marketing strategy to understand their clients and grasp how people think, reason and choose among the various items and services supplied in the market. By effectively understanding its customers and their buying behaviour, a firm can maximize its internet strategy, enhance user experiences, foresee how consumers will respond to new business strategies, retain consumers who are loyal, design marketing strategies to create new consuming markets, and boost sales revenue. The purpose of this research is to investigate the purchasing behaviour of UK based e-commerce retailer customers to enable to provide better services and products. The data analysis conducted on customer data enables understanding of the type of products the customers are likely to purchase based on the description of their previous transactions. Four models based on supervised machine learning techniques were applied, namely Logistic Regression, K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest, and their performance was compared to predict the products that customers are most likely to purchase." @default.
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- W3208261001 date "2021-10-07" @default.
- W3208261001 modified "2023-09-27" @default.
- W3208261001 title "Comparative Study of Machine Learning Algorithms for Product Recommendation Based on User Experiences" @default.
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