Authors: Riccardo Succa, Aleksandr Tukallo, Marco Zoveralli
The dataset consists in 250'000 samples, each with 30 features, of particle collisions conducted ad CERN (Geneva).
This project aims to use machine learning algorithms to predict the decay signature of particle collisions and understand if the event’s signature was the result of a Higgs boson (signal) or some other process/particle (background). No machine learning libraries have been used.
The model can be trained by downloading the dataset (and put it inside a data/ folder) and running the python script run.py. Moreover, the Jupyter notebook sample_notebook.ipynb describes in details the data analisys and the models tried.
MLscripts/: folder containing the python scripts used to load, clean, plot the dataset and the implementation of the baselines.hotgrad/: Neural Network framework.sample_solution.ipynb: jupyter notebook containing all the steps followed, from the data analysis to the tuning and training of the models.