In this demo we shall build a linear regression model from a sample dataset, then create an API for it using flask and then finally build a docker image so that it can be deployed anywhere.
In the folder notebooks, there is a notebook called "demo.ipynb". This is the main content of this demo, so download/open it and follow it along.
My references for building this was: https://www.datacamp.com/community/tutorials/machine-learning-models-api-python https://www.kdnuggets.com/2019/01/build-api-machine-learning-model-using-flask.html https://www.docker.com/blog/containerized-python-development-part-1/ Note that these are slightly out of date as some functions have been depreciated, but in this demo/tutorial I have used up-to-date libraries as of 28/9/2020 that i know of. There maybe other libraries out there that could have been used but as of making this these are the ones I am aware of.