Names : Berezantev Mihaela, Sinsoillier Mike Junior, Du Couédic De Kergoualer Sophie Zhuo Ran

This repository contains the code to produce a model dedicated to classify the datapoints from the CERN particule accelerator data, in order to identify the Higgs Boson.

This repository contains:

• data : contains the dataset for both the training and the testing.
• report.pdf : the report explaining the complete process of this project
• scripts: all the executable code, in particular
  • project1.ipynb: a notebook with various procedures to generate predictive models
  • data_exploration.ipynb: a notebook illustrating our procedure to preprocess the data
  • preprocessing.py: the implementation of the functions actually used to preprocess the data
  • implementations.py: implementation of the different machine learning methods for the prediction
  • cross_validation.py: contains several functions to help tunning the hyper-parameters, as well as validating the error and the accuracy
  • proj1_helpers.py: some helper functions to load and store the data

Make sure you have the following installed on your developement machine:

• python3 - Download & Install python. All the implementation are coded in python3.
• numpy : for an easy manipulations of the data arrays. You can install it via pip.

pip install numpy

To run and produce the predictions on the test data, get into the scripts/ folder and run run.py

cd scripts/
python3 run.py

This will produce a file submission.csv in the data folder. The mean and standard deviation of accuracy using 4-fold is computed as well. The produced file can be submitted on aircrowd for an accuracy score of 0.819 and F1 score of 0.721.