A project which explores the application of supervised machine learning in the search for the Higgs boson.

Presentation: A powerpoint presentation to show the findings of the report in a more digestible manner. Start here to get an overview of the project.

Report: A literature review of five previous research pieces showcasing the most popular and successful machine learning methods used when searching for Higgs bosons from 2014 to 2022. Then, XGBoost and AdaBoost models are implemented using Scikit-learn and evaluated on simulated collision data. XGBoost demonstrates higher precision and AMS scores, whereas AdaBoost scores higher in accuracy and recall.

code-notebook: All the code used for cleaning and preprocessing the particle collision data & implementing the machine learning models.