How to Use Machine Learning to Make Powerful UFC Fight Predictions

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

This article teaches you how to approach and solve a data science project by examining the prediction of UFC fight outcomes.

This article will feature how to make accurate classification predictions for the outcome of UFC fights. It will discuss all the steps involved in making the predictions, from gathering relevant and high-quality data to preprocessing the data, using the data to train machine learning models, and testing the models with different metrics. This will serve as an example data science project, where you can learn how to approach a problem as a data scientist and develop solutions that create value.

Learn how you can predict UFC fight outcomes with this article. Image by ChatGPT. “can you make an example image of an MMA fight where the fighters are segmented out with different colors” prompt. ChatGPT, 4, OpenAI, 3 Mar. 2024. https://chat.openai.com.

My motivation for this project is twofold. First of all, I am a fan of the UFC and regularly watch the fights. Thus, I think it is interesting to see how well a machine learning algorithm would perform in predicting winners from fights. Secondly, I remember talking to someone who was trying to predict UFC fights using computer vision a few years back. I recall wondering how they would even approach the prediction task this way, but I still thought it… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI