Qtext :

Your organization provides various kinds of services to its customers. So$,$ maintaining a customer base and increasing it is very important for your organization to stay competitive in the business. However is concerned that some of the customers might potentially leave. You are a machine learning expert. Your boss approaches you to design a ML classifier to predict customer churn so that some remedial action can be taken. With your expertise, you are aware that a binary classification model can be used to classify whether a customer will leave or stay in the near future. This type of classification model can be found in different business scenarios such as up$-$selling$/$cross$-$selling to existing customers, identifying at$-$risk accounts in the customer base, etc. After collecting necessary data, you trained a binary classifier model which gives very high accuracy on the training data, but much lower accuracy on validation data. What might be happening here? Explain in terms of bias and variance. What are different methods that can be used to rectify the situation. Explain each of the method with examples.

$[5$ Marks$]$