Customer Churn Prediction For Telecom

Abstract

Customer churn prediction is an important area of interest for businesses in competitive and subscription based business models where customer retention is a key determinant for business success. Accurate prediction of customers who are likely to churn can help businesses design proactive strategies for retaining customers and reducing business losses. In this paper, a design and implementation of a Customer Churn Prediction System using machine learning techniques is presented to accurately predict customers as Churned or Stayed and their probability of Churning The proposed system uses a range of customer data, including demographic characteristics, service usage behavior, billing and contract details, and service engagement. A structured data processing   pipeline is designed for accurate data processing and modeling. To overcome common problems in churn prediction models related to class imbalance issues in machine learning models, a hybrid model using SMOTE ENN is implemented for accurate class imbalance handling. Further, a machine learning model using a random forest algorithm is implemented for accurate Predictions. The random forest algorithm is a popular ensemble machine learning technique known for its robustness and high accuracy in dealing with a wide range of data types. The performance of the model can be evaluated using different metrics like accuracy, precision, recall, and F1-score. However, it should be noted that recall should be given prime importance to effectively identify high-risk customers. The proposed system utilizes SQL for storing data in a structured manner and Flask for deploying the application. Interactive visualization has been achieved through Plotly. The proposed system has been designed to effectively cater to the problem of customer churn prediction in an effective manner. The proposed approach utilizes advanced sampling techniques and ensemble learning to effectively predict customer churn, and through the integration of visualization techniques, it can be effectively utilized to take effective decisions regarding retaining customers within an organization.