International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 08 | Aug 2026
p-ISSN: 2395-0072
www.irjet.net
CUSTOMER CHURN PREDICTION SYSTEM USING GRADIENT BOOSTING CLASSIFICATION V.PRAVEEN KUMAR1, DR. G SRIRAM 2, DR. KONDAPALLI VENKATA RAMANA3 1Department of Information Technology & Computer Applications, Andhra University, Visakhapatnam, Andhra
Pradesh, India-530003
2Assistant Professor, Department of Computer Science & Systems Engineering, Andhra University, Visakhapatnam,
Andhra Pradesh, India-530003
3Associate Professor, Department of Computer Science & Systems Engineering, Andhra University, Visakhapatnam,
Andhra Pradesh, India-530003
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2. LITERATURE REVIEW
Abstract - Customer churn prediction is a problem for Banks since it provides information about the customers who are likely to leave in the near future and give them enough time to convince them to stay with the firm. The current work proposes a system for customer prediction using Gradient Boosting Classification, which makes use of the socio- relational attributes of the customer to predict whether the customer will stay or not. Feature engineering and training were carried out to ensure that the system has accuracy. A web application was also developed so that the users could interact with the system and make predictions, Probabilities of customers going away. Visualizations WERE used to inform decision-making. The system was. Found to have an accuracy of 87.00% with precision of 79.28%, 48.89% recall and 60.49% F1 score which implies that the system can be used to reduce customer attrition.
Many researchers have used machine learning algorithms in the prediction of customer churn in the years. Some of them have reported that the logistic regression analysis, decision trees and random forest classification have performance when analyzing customers’ data and predicting their attrition. In fact some works have demonstrated that combining algorithms can give better results because different algorithms have diverse patterns and structures which help in capturing complex relationships within the data. Researchers have also demonstrated that Gradient Boosting Classification technique is better than others in modeling and predicting interactions.
Keywords: Customer Churn Prediction, Gradient Boosting Classifier, XGBoost, Feature Engineering, Streamlit, Confusion Matrix, Predictive Analytics.
In the work Gradient Boosting Classification is used to predict customer attrition. A system which facilitates analyzing and predicting customer attrition based on their characteristics is introduced. The system was built by carrying out data preprocessing followed by a training process. Finally a web application was developed for facilitating the prediction process by the end-users.
1. INTRODUCTION Customer churn prediction is a problem of interest to organizations since it provides the information about the customers who are likely to switch to other entities in the near future. This information is useful to the companies because they can take measures to retain the customers by convincing them to stay with them instead of taking their services or products to other firms. The main idea behind customer churn prediction revolves around reducing the attrition rate of the existing customers since it's cheaper to retain than acquire new customers.
3. PROPOSED METHODOLOGY The current work presents a system based on Gradient Boosting Classification for predicting customer attrition. The proposed system consists of the following steps: obtaining and preparing data, processing data training the model and prediction using a web-based interface. 3.1 Dataset
The current research presents a system based on Gradient Boosting Classification for predicting customer attrition. Using the attributes such as a credit score, demographics, location, gender, tenure and account balance among others to predict customer attrition. A web application was also developed to facilitate the prediction process of analyzing customer data and predicting their attrition.
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A sample customer dataset was obtained which contains socio- demography characteristics that help in predicting customer attrition. The data had ten thousand records with attributes such as: credit score, gender, age, tenure, balance, annual salary and many others.
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