A Hybrid Deep Learning and XGBoost Framework for Predicting OTT Subscription Timing Based on user Behavior Analysis

Puntos clave

• Subscription timing is effectively predicted using a hybrid deep learning model and XGBoost.
• The model demonstrates a high accuracy rate of 85% in forecasting user subscriptions.
• Analysis used a combination of machine learning techniques to assess user behavior and predict outcomes.
• The framework supports more efficient user targeting in subscription-based services, highlighting potential market opportunities.