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A simple walkthrough of using AWS Machine Learning

AWS Maching Learning Service Walkthrough

This is a simple walkthrough intended to introduce Amazon's Machine Learning service in the context trying to predict loan interest rates and grades. This is a managed service that can be useful in basic regression models used for predictions.

AWS Machine Learning

We’re going to use some publicly available loan applicant data from Lending Club. The data set I’ve chosen contains over 420,000 loans with data such as applicant annual income, loan term, state of residence, credit score range and a number of other credit data, etc. The dataset also contains a letter grade classification of the loan in the range A - G. We’re going to use AWS machine learning to build two models, one that will predict an appropriate interest rate and a second that will infer the letter grade for the loan application.

  1. Creating a Datasource
  2. Data Cleaning and Feature Engineering
  3. Creating the Machine Learning Model
  4. Gotchas and Limitations

Custom AWS Machine Learning Pipeline

AWS Machine Learning Pipeline This repo also outlines how to create and deploy a custom machine learning pipeline using Keras (with Tensorflow backend) and AWS Lambda. It uses the same loan dataset but ends up with much more accurate results through the use of a neural network. See the Machine Learning Pipeline README for more details.