ИСПОЛЬЗОВАНИЕ МАШИННОГО ОБУЧЕНИЯ ДЛЯ ПРОГНОЗА ОТТОКА В ТЕЛЕКОММУНИКАЦИОННОЙ ОТРАСЛИ

Abstract

This article describes how effectively a deep learning approach can be used for the churn forecasting process in the telecommunications industry with greater accuracy and less processing time. It is observed that data mining systems are gradually succeeding in predicting customer churn over the previous couple of years. Developing a powerful churn forecasting model is a critical task that involves a lot of research directly from recognizable proof of ideal performance, from the vast amount of customer information available to the selection of a successful information mining system that matches the list of possibilities.