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Kumar, S. and Kumar, M. (2019, May) Predicting Customer Churn Using Artificial Neural Network. In: Macintyre, J., Iliadis, L., Maglogiannis, I. and Jayne, C., Eds., EANN 2019: Engineering Applications of Neural Networks, Springer, Cham, 299-306.
https://doi.org/10.1007/978-3-030-20257-6_25

has been cited by the following article:

• TITLE: The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

  AUTHORS: Mohammed Ali Alzahrani

  KEYWORDS: Customer Churn, Machine Learning, Amazon Fine Food Reviews, Data Science, Artificial Intelligence

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.12 No.1, February 8, 2024

  ABSTRACT: Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.