Harnessing deep learning to forecast customer churn in telecommunication

Losing customers, or customer churn, is a major problem for companies that has an impact on growth and revenue. With the help of deep learning techniques, this research seeks to precisely identify which customers are most likely to depart, allowing businesses to take proactive measures to retain them. This study uses sophisticated neural networks, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to identify trends in consumer interactions and behavior. To guarantee high predicted accuracy, the process includes feature engineering, data preparation, and model optimization. Effectiveness will be assessed using performance indicators like F1 score, recall, and precision. Additionally, interpretability methods like SHAP (Shapley Additive explanations) will pinpoint the primary causes of attrition, offering useful information to direct client retention tactics. KEYWORDS: RNN, CNN, interpretability, SHAP, deep learning, customer churn.