Churn Rate Prediction System using Logistic Regression, SVM, and Random Forest Models

Customers are the life blood of any business, and their importance cannot be overstated. Customers are a business’s primary source of revenue. Retaining and gaining new clients is crucial to profitability. Over time, repeat customers typically spend more, and gaining new customers is frequently more expensive than keeping current ones. Consequently, establishing a foundation of devoted clients might boost revenue. Without customers purchasing products or services, there would be no income to sustain the business operations or drive growth. It might have several detrimental implications, such as a decline in market share, loss of revenue, bad impact on staff morale, etc. To tackle this problem supervised learning algorithms are trained on historical data, where the input features and corresponding churn labels are known. The goal is to learn patterns from the data that can help and predict churn accurately for newly unseen data. These algorithms guarantee a high degree of accuracy when forecasting customer attrition, allowing businesses to precisely identify consumers who pose a danger. Businesses frequently use focused tactics, such as re-engagement campaigns with discounts or offers that are specifically catered to the requirements and interests of each churned consumer, to entice and re-engage these customers through individualized communication.