Problem statement

A financial institution wants to predict the interest rate on personal loans based on various factors such as income and credit details, loan amount, term, and debt-to-income ratio. It has collected data on these factors from past loans and would like to build a regression model that can accurately predict the interest rate on future loans. It has built a regression model with H2OXGBoostEstimator, getting an RMSE score of about 3.95 on the test dataset.

Your task is to find the right set of values for the given parameters, which should bring down the RMSE value to <3.80. Only use the given parameters—do not use any additional ones.

Data description

The financial institution has provided a dataset of past loans, which includes the following variables:

emp_length: The applicant’s number of years at their current job, rounded down to the nearest year. Values are capped at 10.
state: The two-letter code for the state where the applicant resides.
...

Introduction to Machine Learning

Supervised Learning: Regression Models with H2O

Supervised Learning: Classification Models with H2O

Unsupervised Learning: Clustering with H2O

Unsupervised Learning: Anomaly Detection with H2O

Closing Notes

Appendix

Challenge: Improve H2OXGBoost Regression Model

Problem statement

Data description