Prediction Of Churning Customers for Bank and Telecommunication Sectors
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Keywords
KNN, AUROC, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest
Abstract
In this paper the prediction of churn customers and non-churn customers were done. Basically a single customer is very important for an industry, or for organization etc... This project will predict or identify the churn customers and the non-churn customers in the given dataset of the organization. Some Machine Learning algorithms are used in this project to predict the churn customers range and accuracy of the algorithm in finding the churn rate. We have created a web based app, which requires some details about the customers of the organization to predict whether Churn or Non churn customer.
References
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