Main Article Content
In the telecommunications industry that is working with a different number of subscribers or consumers daily, the dividends of the company are mostly dependent on the payments provided by these subscribers. Because it has been observed that the subscribers get frustrated sometimes with the services as well as the response of the company to their queries and based on those situations the subscribers decide to stop using the services of the organization or shift to using other services that might provide less revenue to the organization ultimately resulting in the organization’s losses. For this problem faced by the organizations, our project intends to find out the factors that influence the subscriber’s mindset while taking decisions related to the services of a particular telecom organization and use the same to predict whether new subscribers will behave in the same manner or not. Our project initially focuses on using Exploratory Data Analysis to gather important factors related to the type of customers who can churn out in the company. Followed by that after getting the insights, we will be building the model using certain machine learning algorithms that will predict whether the customer will churn out or not. The last step will be implemented by deploying the model using Flask for other users to access it and try it out.