A Predictive Analysis Model of Customer Purchase Behavior using Modified Random Forest Algorithm in Cloud Environment

Abstract

Prediction analysis of customer purchase behavior is an interesting and challenging task in modern-day life. Our objective is to introduce the concept of machine learning using a random forest algorithm in depth. In this paper, a model has been proposed for predicting which cloud services have been purchased on a number of factors. A random forest model is built using different parameters such as advertisement click sequence, previously purchased cloud services, etc. and training our model. For the execution of this proposed model, an advertisement log dataset has been taken and the necessary modifications have been done on it. As an outcome, for a customer, this proposed model is providing high accuracy in prediction. Five factors have been considered which affect purchasing decision-making of customers in cloud services, such as previous purchasing habits, a sequence of advertisements viewed, customer location, etc..