Application of Machine Learning Algorithms to Predict New Mobile Customers

Because of economic liberalization and globalization, the nature of business competition has undergone significant change. New technology’s rise in corporate operations has more competition and created fresh opportunities for service suppliers. Businesses are shifting their attention away from finding new customers and towards keeping their existing clients to deal with the escalating difficulties. This uses less energy since it is more cost-effective. In this part, we take a look at prospective mobile internet users by using machine learning as well as deep learning methods to data on consumer behavior and usage. This should help us identify new mobile internet users. This research looked at mobile internet users in the telecommunications sector using user behavior and comparable data from a provider of telephone services. Decision Trees and Random Forest ensembles for support vector machines are employed. To build a binary classification system for mobile internet use, these models serve as foundational statistical machine learning tools. In this research, machine learning algorithms are used to analyze user behaviors in a distributed computing environment. Users in these groups all exhibit similar behaviors. The user’s behavior-related events are recorded and added to a database.