The loss of customers, commonly referred to as attrition, is one among the most significant issues the telecoms industry has to deal with. Getting new customer costs more than maintaining an old one. Data analysis can be used to identify customer churn causes and retain customers by collecting data from telecom companies. Therefore, for telecom firms to retain their consumers, churn forecasting is essential. In order to keep their current clients, telecom providers need to be aware of what generates churn. The knowledge needed to gain this information can be extracted from telecom data. We will develop and analyze a number of machine learning models that are supervised in this, and we can say that Random Forest performs the worst compared to others because they offer greater accuracy.