Customer Churn Detection for insurance data using Blended Logistic Regression Decision Tree Algorithm (BLRDT)
Keywords:
Customer churns detection, preprocessing, Z-score technique, Blended Logistic Regression Decision Tree (BLRDT) algorithmAbstract
Customer churn identification is indeed a subject in which machine learning has been used to forecast whether or not a client will exit the corporation. Predicting client turnover is a difficult issue in a variety of businesses, with the financial sector being the most well-known. Because of the constant updating of insurance plans, the client retention procedure is critical to the company's success. Predicting client turnover and developing customer retention strategies are both key research areas in the insurance market. This identifies the importance of detecting churn rate in the insurance industry. The primary objective of this research is to forecast consumer habits and to distinguish between churners and non-churners during an earlier phase. We propose a Blended Logistic Regression Decision Tree (BLRDT) framework for churn detection. Initially, the preprocessing of the insurance dataset is done by employing the Z-score technique. Data splitting is done to separate the standardized dataset into training and testing sets. The churn prediction is done using the BLRDT algorithm. The evaluation was done by using different measurements like accuracy, precision, recall, and f1-score and the outcomes are depicted by employing the MATLAB environment. The research also discovered that the presented scheme seems to be a realistic and slightly superior strategy for the insurance sector to estimate client turnover than the findings obtained using existing approaches.
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