Train and Build a Classification Machine Learning App Using Loan Data

Train and Build a Classification Machine Learning App Using Loan Data

Train and Build a Classification Machine Learning App Using Loan Data. A brief guide to building an app to serve a classification model, containerizing that app and deploying it.

Follow along with all of the code used in this project here!

We used GCP to code this project, which comes configured with Docker. If you are interested in following along with a pre-configured cloud VM check out this article to get started!

Our data is taken from a book known as ISLR. Check out this work for information on machine learning and statistics!

Problem

We can imagine a bank would like to know who is a safe loan recipient, but how can we tell if someone is trustworthy while only knowing a few things about them?

Our data set contains rows representing individuals with default status and some other information we will use to predict the default status. In other words, this is supervised learning.

First, we will clean up the data so that it is better suited for a classification model.

Data

Let’s have a look at our data.

Image for post

Raw data

It looks like we have four columns and an index, including bank balance, yearly income, student status and loan default status. This data can be cleaned to better suit a classification model, so let's make some changes.

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Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

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