CUSTOMER CHURN PREDICTION USING DATA SCIENCE

Abstract

Customer churn prediction has become a critical analytical task for organizations that rely on longterm customer relationships, particularly in industries such as telecommunications, banking, and subscription-based digital services. Churn occurs when customers discontinue using a company’s products or services, leading to reduced revenue and increased costs associated with acquiring new customers. Studies indicate that retaining existing customers is significantly more cost-effective than acquiring new ones, making churn prediction an essential business strategy [1]. With the rapid growth of data science and machine learning, organizations are increasingly leveraging predictive analytics to identify customers who are at risk of leaving and implement targeted retention strategies [2]. This research presents a data-driven customer churn prediction system that analyzes historical customer data and behavioral patterns to estimate the likelihood of churn. The proposed system employs various machine learning techniques including Logistic Regression, Decision Tree, Random Forest, and Artificial Neural Networks to build predictive models capable of detecting potential churners with high accuracy. The dataset undergoes several preprocessing stages such as data cleaning, handling missing values, encoding categorical attributes, and feature scaling to ensure reliable model performance [3]. The models are evaluated using standard classification metrics including accuracy, precision, recall, and F1-score. Emphasis is placed on recall to ensure that the maximum number of churn-prone customers are correctly identified. The system also incorporates model interpretability methods to highlight the key factors influencing churn behavior. Experimental results demonstrate that machine learning techniques can significantly improve churn prediction performance, enabling organizations to develop proactive customer retention strategies and enhance long-term business sustainability.