Enhancing Customer Churn Analysis in the Banking Sector through Machine Learning Algorithms and Hyper Parameter Tuning

Abstract

In the dynamic landscape of the banking sector, understanding and mitigating customer churn is crucial for maintaining a stable and profitable customer base. This study explores the application of machine learning algorithms, namely Random Forest, Decision Tree, Gradient Boosting, and Artificial Neural Networks (ANN), for predicting and analyzing customer churn in the banking industry. The research focuses on evaluating the performance of these algorithms both before and after hyperparameter tuning, aiming to enhance predictive accuracy and model robustness. The initial phase involves preprocessing and feature engineering to optimize the input data, ensuring the algorithms receive relevant and meaningful information. Subsequently, four distinct machine learning algorithms are employed to build predictive models. The Random Forest algorithm excels in ensemble learning, Decision Tree offers interpretability, Gradient Boosting provides boosting techniques, and ANN leverages neural networks for complex pattern recognition. To improve the models' performance, hyperparameter tuning is conducted, involving an exhaustive search for optimal hyperparameter configurations. This process aims to fine-tune the algorithms and maximize their predictive power. Comparative analyses are then performed to measure the effectiveness of the algorithms before and after hyperparameter tuning, using metrics such as accuracy, precision, recall, and F1-score. The results demonstrate the impact of hyperparameter tuning on model performance, highlighting improvements in prediction accuracy and robustness. The findings contribute valuable insights to banking institutions seeking to implement effective customer churn prevention strategies. The study's methodology and results serve as a guide for leveraging machine learning algorithms and hyperparameter tuning in other industries facing similar customer retention challenges.