Customer Churn Analysis Using Feature-Based Decision Classifier

To survive in the highly competitive marketplace, customer retention forms the backbone of any business for long-term success. Being able to identify customers that are at risk of churning allows the organization to take measures in time to decrease client attrition and optimize customer lifetime value. In this regard, the work creates a broader system for churn prediction that entails machine learning algorithms-logistic regression, random forest, K-nearest neighbors-and a custom classifier based on decision rules tailored for ad hoc business conditions. It involves interfacing with multiple databases where the user can open datasets from either their driving machines or retrieve from online sources, thus providing users the opportunity to work in varied business environments. After predicting churned customers, this system has implemented automated email notifications to resettle the customers, thus improving retention efficiency. Detailed geographical analysis based on customers' pin codes will explain churn patterns across regions for further action points on targeted approaches and resource deployment. Evaluating the performance of the system on several models and scenarios in this work suggests the system as having further potential for being proven scalable and adjustable for making businesses of all sizes work. The results show that this approach works most definitely to the advantage of the SMEs in improving their retention strategy through predictive analysis and automated engagement. Keywords: Customer Churn, Machine Learning, Logistic Regression, Random Forest, K-Nearest Neighbors, Automated Email Notifications, Pin code Analysis, Customer Retention Strategies.