Abstract: Customer churn is a major challenge in subscription-based platforms, where customers discontinue their services, leading to revenue loss. This project aims to predict customer churn and understand the key factors influencing customer behavior so that businesses can take actions to improve customer retention. In this project, a subscription-based customer dataset will be used, which includes features such as subscription type, usage patterns, login frequency, and payment details. The data will be preprocessed by handling missing values and encoding categorical variables. Exploratory Data Analysis (EDA) will be performed to identify patterns and trends related to customer churn. Machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and XGBoost will be implemented to build predictive models. A comparative analysis will be carried out using evaluation metrics such as accuracy, precision, recall, and F1-score to identify the best-performing model. XGBoost is included as an advanced algorithm because it improves prediction accuracy by combining multiple weak models and handling complex data patterns effectively. By performing this analysis, the project will identify high-risk customers and the key factors leading to churn, enabling businesses to design effective retention strategies such as personalized offers and improved customer engagement. As an enhancement, an interactive dashboard will be developed to visualize churn patterns and monitor customer risk levels for better decision-making.

Keywords: Customer Churn Prediction, Machine Learning, XGBoost, Model Comparison, Subscription-Based Platforms

Downloads: | DOI: 10.17148/IJIREEICE.2026.14431