# 线性回归代写｜Linear regression: Bank Loans

Here, you will employ any ** linear regression** skills and ideas. The suggested approach is to start with an EDA). This may give you an idea about the potentially useful predictors, transformations, and interactions, etc. Then, try other approaches to further improve your model, such as cross validations, variable selection, etc. Any skills and techniques can be used as long as it is a linear model. Be sure to set seeds for any methods relying on randomness).

The data consists of loan applications, including information on applicants and the amount of money made from each application. Your job is to predict the amount of money made by the bank for an application they are considering.

Requirement

Evaluation Metric: root mean squared error (RMSE)

Discover the INSIGHT such that:

You need to discover the * insight*, using which one can make highly accurate predictions on more than half of the train / test data. While it is not easy to get a

*in this problem, once you discover that*

*RMSE of less than 650**, you can get*

*insight**more than half of the train data (while also being confident that you are not overfitting on that part of the train data). Note: you cannot compute RMSE on a specific part of the test data as you don’t have the test response.*

*a RMSE of only 5 on*