Machine Learning Approach to Detect Online Shopping Addiction

Abstract

Online shopping addiction is the rapidly growing phenomenon. The excessive or uncontrollable buying over the internet can be defined as online shopping addiction. Compulsive buying disorder is the mental health problem that associated with shopping addiction. This addictive situation directly affected to the consumers and caused to several negative impacts. This study focuses on detect online shopping addiction by considering consumers’ motivation towards attractive features and facilities provided by the online shopping environment. The data were collected through online questionnaire and 511 data were used for carry out this research. The Multilayer Perceptron (MLP) neural network, SVM, Naïve Bayes, Random Forest and Decision Tree algorithms were used to develop the models. For the neural network model there were 11 attributes in the input layer and 2 classes in the output layer. Then add one hidden layer with 13 neurons to train and build the model. The accuracy of the build MLP model was 90.90% and it was the highest accuracy compared to the other developed models. This machine learning model can find out the addiction towards online shopping.