Linear Regression with AWS SageMaker

I recently came across one of the new products from AWS — Amazon SageMaker.

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.

Now I’ve used some of the ML models that AWS has provided in the past for linear regression and wasn’t entirely overwhelmed, however SageMaker has a couple of features that look really promising.

Perhaps the best feature of SageMaker is hosted Jupyter Notebooks. I love the integration of markdown and graphs/visuals with my code when exploring models.

I should preface this tutorial with two statements:

1. I claim no expertise in Python or ML. If you come across an error, misunderstanding or “bad” way o f writing Python please let me know. I write ML algorithms as a hobby, not as a career.
2. I’m writing this as I’m looking at SageMaker for the first time, so there may be “better” ways of doing things.

Anyway let’s get stuck into a simple example of linear regression with SageMaker!

What We Will Build

When testing pre-built ML algorithms, I like to perform a run to test the convergence/performance of the model by giving it data that I know has a perfectly linear relationship.

In this example I am going to take some AFL (Australian Rules Football) Match Data and try to…