Neural Networks Predictive Analytics: A Case Study on Bank Customer Retention
Abstract: Customer retention plays a crucial role in sustaining business profitability and ensuring long - term success. Retaining existing customers is often more cost - effective than acquiring new ones. This research explores how Neural Networks (NNs) can be effectively used to predict customer churn, enabling businesses to take proactive measures to retain high - risk customers. The study implements structured data preprocessing, exploratory analysis, model optimization, and evaluation techniques to enhance prediction accuracy. Various machine learning frameworks such as Scikit - learn, TensorFlow, Keras, and Pandas are utilized to preprocess data, train models, and evaluate performance. The experimental outcomes demonstrate a significant improvement in predictive accuracy, allowing businesses to make data - driven decisions. Furthermore, the study presents actionable recommendations to optimize customer engagement strategies and resource allocation.
Keywords: Neural Networks, Customer Retention, Predictive Analytics, Customer Churn, Machine Learning, Data Preprocessing, SMOTE, Multilayer Perceptron, Adam Optimizer
How to Cite?: Narendar Kumar Ale, "Neural Networks Predictive Analytics: A Case Study on Bank Customer Retention", Volume 14 Issue 3, March 2025, International Journal of Science and Research (IJSR), Pages: 1038-1041, https://www.ijsr.net/getabstract.php?paperid=SR25322080021, DOI: https://dx.doi.org/10.21275/SR25322080021