Customer Churn Prediction Using Machine Learning Techniques: Awash Bank Wolaita Sodo Region

Customer churn prediction refers to the procedure of identifying customers who are highly likely to terminate their service subscription based on their utilization. Being able to predict a customer who is likely to churn is essential for solving business problems. The banking industry in Ethiopia currently has millions of users, making it challenging to analyze and anticipate customer attrition. There are diverse researches conducted in this particular domain. The primary challenges encountered in the majority of the prior investigations were associated with the selection of suitable technique for achieving data balancing, the predicaments revolving around the choice of a technique for handling missing values, the excessive dependence of the model on a singular attribute, and various others. The aim of this research is to develop a machine-learning model that can predict customer churn. The dataset utilized for this investigation comprises 50,987 entries encompassing 11 attributes, which were collected from Awash Bank Wolaita Sodo region. Among these, 31,619 represent active accounts, while the remaining 19,368 pertain to closed (churn) accounts. To achieve balance within the dataset, a SMOTE-ENN method is employed, while an extraction tree classifier is employed for important feature selection. This research used an experimental research approach, and eight model are tested, including Extreme Gradient Boosting (XGBoost), random forest, Light Gradient-Boosting Machine (LightGBM), decision tree, Convolutional Neural Network (CNN), Gradient Boosting Machine (GBM), Deep Neural Network (DNN), and Multilayer Perceptron (MLP). Model performance is evaluated using accuracy, f1-score, recall, and precision. Experimental results show random forest model outperformed other models with an overall accuracy of 99.14% and recall, precision and f1-score of 99%.