customer churn prediction using recurrent neural network and long short term memory network

K.C.Asmitha,Stellamarry.S,Sandhiya.S

Published in International Journal of Advanced Research in Computer Networking,Wireless and Mobile Communications

ISSN: 2320-7248          Impact Factor:1.8         Volume:5         Issue:3         Year: 17 April,2025         Pages:236-241

Abstract

Customer churn prediction is a critical aspect of customer retention strategies in industries such as telecommunications, banking, and ecommerce. The goal of this project is to predict the likelihood of customers discontinuing services by analyzing historical customer data using advanced deep learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. These models are wellsuited for time-series data and sequential patterns, making them ideal for detecting churn trends in customer behavior over time. In this project, a dataset containing historical customer information such as demographics, transaction history, usage patterns, and service interactions is used to train the RNN and LSTM models. By learning patterns from sequential data, the models can capture dependencies in customer behavior and predict the probability of churn. The RNN helps in processing sequences of customer interactions, while the LSTM addresses the issue of long-term dependencies in the data, enhancing prediction accuracy.

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Kewords

Customer Churn, Churn Prediction, Customer Retention, Predictive Modelling, Machine Learning

Reference

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