Comparisons Of Data Mining Classification Algorithms For Customers' Shopping Intention In E-Commerce

Online shopping provides an excellent opportunity and platform for today's traditional businesses. The application of data mining for online shopping provides helps in understanding consumer behaviour, purchasing patterns and increased customer experience. The aim of this study is to identify the potential factor that affects customer to purchase on e-commerce and classify the potential customer by using various single and ensemble method classification algorithms. The experimental results revealed that Random Forest has the best performance in term of accuracy, precision, recall, F1, and Receiver Operating Characteristic Curve. However, Logistic Regression has the lowest computational time as compares to other algorithms. The resulting model provides a different selection of model into classification model of a potential customer which directly benefit the company to select the suitable model in lowering the cost and time when providing more personalized customer experience for the customer.