SCRS Proceedings of International Conference of Undergraduate Students
Hybrid Model for the Customer Churn Prediction
Authors: Mansimar Anand, Irtibat Shaukat, Harnoor Kaler, Jai Narula and Prashant Singh Rana
Publishing Date: 09-01-2023
ISBN: 978-81-95502-01-1
Abstract
The fast development of the showcase in each segment is driving to a prevalent endorser base for benefit suppliers. In such a quick setup, benefit suppliers have realized the significance of holding the on-hand clients. It is in this manner fundamental for the benefits suppliers to prevent churn - a phenomenon that states that the client wishes to quit the benefit of the company. The key here is to be motivated and have interaction with these clients. While simple, in theory, the realities worried about achieving this “proactive retention” goal are incredibly challenging. The most commitment of our work is to create a Churn expectation show that helps companies foresee clients who are most likely subject to churn. The model developed in this work employs machine learning strategies on the dataset and builds a robust training and testing model. The proposed model results are authenticated using K-fold cross-validation, and an accuracy of 91.48% is achieved. The main contribution is using K-means clustering to make clusters and then applying the Random Forest classifier for model prediction. The model was organized and tested operating on a data set created and supplied by a telecom organization from the United States of America. The model experimented with seven algorithms: Random Forest, Logistic Regression, Naive Bayes, K-nearest neighbors, Gradient Boosting, Ada Boosting, and K-means. However, the proposed model is experimented with by combining the K-means and Random Forest algorithm.
Keywords
Machine Learning, Hybrid Model, Predictive Model, Feature Selection, Customer Churn.
Cite as
Mansimar Anand, Irtibat Shaukat, Harnoor Kaler, Jai Narula and Prashant Singh Rana, "Hybrid Model for the Customer Churn Prediction", In: Prashant Singh Rana, Deepak Bhatia and Himanshu Arora (eds), SCRS Proceedings of International Conference of Undergraduate Students, SCRS, India, 2023, pp. 85-94. https://doi.org/10.52458/978-81-95502-01-1-9