machine learning tutorial

machine learning tutorial

Data scientists often have to communicate results to other people. In my case, my supervisors might want to see some numbers or I have to write up the main insights of some work for a paper. This is pretty straightforward — I just copy and paste into an email or a Latex document. But what if I want to send someone an actual model that I have trained, so that they can either evaluate or use it? If the person in question is technical enough, then I can just save the model to disk and email that, along with some Jupyter notebooks.

But what if you want someone in marketing to try out a model, or you want to share it with friends who might find it interesting? In this case, you’ll need to make your code accessible to them in a way they are familiar with.

The three parts to this tutorial are:

Creating a simple model that can be deployed to the web, where users can input variables to get predictions.

Building the components needed by Flask microframework to create a web app.

Deploying the final web app using the Heroku platform.

The final product will look like this: http://bike-model.herokuapp.com/ — and if you know CSS (which I find much harder than machine learning, for some reason) then you could easily make it look a lot fancier!

There are a lot of tutorials for Flask online, but they can be a bit overwhelming and tend to focus on toy examples like a blog app or a to-do list, with many features we don’t need. In this guide, the focus is on exactly the things you need in order to get your model up and running on the web as quickly as possible.

The model we will deploy is is based on this Cambridge Spark tutorial, using the xgboost package. Our model will perform regression on the data from this tutorial, using three environmental variables (temperature, humidity, windspeed) to predict the number of bikes that will be rented out from a bike sharing scheme. However, you could deploy any model you like!

Software requirements

Make sure you have the following installed if you want to copy and paste code from this page:

Python 3.6+

python packages:

Flask

Pandas

Sklearn

Xgboost

Seaborn

Matplotlib

You will also need the following tools installed:

Git

The Heroku CLI

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machine learning tutorial