In an industry with stiff competition among individual organizations, Churn is an important factor to be considered by the company itself as well as by prospective customers. In the telecommunication industry, churn can be affected by multiple factors: a customer's preference, location, job, and so on. Thus, customer churn in the telecom industry is a widely studied subject. However, churn based on previous rates alone is not enough to predict future churn and there must be additional factors considered. We propose a method to overcome this inadequacy. By using the predictions generated by past data, a rough estimate of the churn rate for a certain time period can be generated, such as a quarter or a year. A Machine Learning algorithm can be used for the same to get the value of the prediction. This value can be further tweaked by incorporating customer feedback which can affect the churn rate. Thus, the value generated by the predictions and the feedback will be more accurate.