Project Idea:

Customer churn is a major problem and one of the most important concerns for large companies.  Telecommunication industry always suffers from very high churn rates when one industry offers a better plan than the previous there is a high possibility of the customer churning from the present due to a better plan in such a scenario it is very difficult to avoid losses but through prediction, we can keep it to a minimal level.

Due to the direct effect on the revenues of the companies, companies are seeking to develop means to predict potential customers to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce it. 

Solution Requirements:

Churn prediction helps in identifying those customers who are likely to leave a company. The main contribution of our work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. 

Build & Deploy a Machine Learning model to predict the customer churn using IBM Watson Studio and predictions can be obtained by using its Endpoint. Create a python - flask application that interacts with the model.

Proposed Technical Architecture: