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Team MLproj1

PROJECT 1 : The Process of Discovering the Higgs Boson Particule

The Higgs boson machine-learning challenge aims to explore the potential of advanced machine-learning methods to improve the analysis of data produced by the experiment. This project relies on high dimensional data and targets the creation of a binary classifier that classifies events as an observation of the decay of the Higgs particle into two tau particles.

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Folders and Files Structure

  • project1_description.pdf : Project guidelines.

  • project_report : Written report highlighting the most important findings obtained.

  • data : provides two files in the .csv format : test.csv (test set) and train.csv (training set).

  • scripts : Provides all the scripts that are needed to implement the project's methods.

    • run.py : Runs the algorithms and provides the predictions in the output folder.

    • implementations.py : Provides all required machine learning methods.

    • proj1_helpers.py : Provides the helper methods used by the code : loading the data, loss and gradient computation....

    • data_processing.py : Provides methods for preprocessing the dataset before using any algorithm on it.

    • optimisation.py : Provides the optimal degree and lambda using k-fold cross-validation (10-fold).

    • validation.py : Provides methods used to execute cross-validation (Data split).

    • parameters.py : Contains the parameters we used.