At the time of the Rugby World Cup in 2019 I did a small data science project to try and predict rugby match results, which I wrote about here. I’ve expanded this into an example end-to-end machine learning project to demonstrate how to deploy a machine learning model as an interactive web app.
Goal
To provide a high-level overview of the key steps needed in going from raw data to a live deployed machine learning app.
Once you’ve gone through this — pick a topic that you’re interested in, find some data, get your hands dirty and aim to build your own machine learning app, from data preparation to deployment.
The key steps
Data wrangling with Pandas & data storage with SQLite
Machine learning (Neural Network) with Keras
Web app with Flask (and a bit of CSS & HTML)
App deployment with Docker and Heroku
The code for this is available on GitHub here and the live app can be viewed here. (Note that this code isn’t necessarily production level, but meant to show what can be done as a starting point. The live app uses a snapshot of data at a specific point in time).

#data-science #machine-learning #python #predictions #flask