XGBoost in Amazon SageMaker

SageMaker is Amazon Web Services' (AWS) machine learning platform that works in the cloud. It is fully-managed and allows one to perform an entire data science workflow on the platform. And in this post, I will show you how to call your data from AWS S3, upload your data into S3 and bypassing local storage, train a model, deploy an endpoint, perform predictions, and perform hyperparameter tuning. The data cleaning and feature engineering code are derived from this blog post, which is written by Andrew Long, who gave full permission to use his code. The dataset can be found here. Head over to your AWS dashboard and find SageMaker, and on the left sidebar, click on Notebook instances .