Hello Aliens. In this, we will be going through the complete life cycle of a ML project. However, the main focus is on the front end which can be done very easily using Streamlit. This is one of the coolest platforms available which makes the life of a ML Engineer, data Scientist’s life easy. So, Lets begin the show.
The problem statement is Bank Churn attrition. The data set is from Kaggle and is available here. This has become very crucial now a days for the business. Let me throw some light on the what is the problem statement and why it needs to put to a check.
Customer attrition is the concern of most business that are involved in low switching cost markets. Banking industry can be one of the top most sufferers with a higher churn rate. the capability to predict that a customer is at high risk of churning, while there is still time to prevent this is a huge additional potential revenue source for every business. Some studies confirmed that acquiring new customers can cost five times more than satisfying and retaining existing customers. As a matter of fact, there are a lot of benefits that encourage the tracking of the customer churn rate as marketing costs to acquire new customers are high. Therefore, it is important to retain customers so that the initial investment is not wasted.
Basically this is of two steps.
- Building ML models and saving the model.
- using the saved model and creating a web application
#frontend #hyperparameter-tuning #streamlit #ui-design #machine-learning
