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Analysis Study Research Paper | Computer Science and Information Technology | United States of America | Volume 14 Issue 3, March 2025 | Popularity: 4.8 / 10
Neural Networks Predictive Analytics: A Case Study on Bank Customer Retention
Narendar Kumar Ale
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
Edition: Volume 14 Issue 3, March 2025
Pages: 1038 - 1041
DOI: https://www.doi.org/10.21275/SR25322080021
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