GaiaFramework/churn_rate_ecommerce

The E-commerce Customer Churn Rate Prediction model is a cutting-edge machine learning solution designed to identify and predict customer churn in online retail environments. This model utilizes a gradient-boosted decision tree framework, specifically XGBoost, to deliver highly accurate and actionable insights into customer retention dynamics. Tailored for e-commerce platforms, it analyzes a rich dataset of customer interactions—such as purchase history, browsing behavior, cart abandonment rates, session frequency, and demographic details—to forecast the likelihood of a customer discontinuing their engagement with the platform. The model begins with comprehensive data preprocessing, handling missing values, encoding categorical variables, and normalizing numerical features to ensure optimal performance. Feature importance is derived from variables like recency of purchases, average order value, and customer support interactions, which are fed into the XGBoost algorithm. This gradient-boosting approach iteratively builds an ensemble of decision trees, optimizing for precision in classifying customers as "likely to churn" or "retained," while minimizing false positives. Output is presented as a churn probability score for each customer, enabling businesses to prioritize retention efforts effectively. The model also supports a churn rate visualization, aggregating predictions across customer segments to display trends over time. With its scalability and adaptability to diverse e-commerce datasets, this solution empowers online retailers to reduce churn, enhance customer lifetime value, and refine marketing strategies through data-driven decision-making.