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.
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