The goal of this project was to apply machine learning techniques to actual CERN particle accelerator data to recreate the process of “discovering” the Higgs particle. Using the different physical properties of the particles’ collisions, we had to determine whether or not a Higgs boson was present. To solve this binary classification task we first preprocessed the data, by removing irrelevant features and transforming others, while splitting the dataset in relevant categories. We then used different regression methods, hyper-parameter tuning and cross-validation to find the most accurate model. We achieved the best results using Least Squares, but maybe some more computer power could have allowed us to better tune our logistic models.
- Python (only tested on >= 3.9)
- Numpy (only tested on >= 1.21)
- Matplotlib (only tested on >= 3.4)
- Seaborn (only tested on >= 0.11)
Before running the python file, you must extract data/train.csv.zip and data/test.csv.zip as data/train.csvand data/test.csv respectively.
To create a submission file for aicrowd.com, you must go in the scripts directory and run python3 run.py <function_number> where <function_number> is used to selected the function with which we want to run the algorithm :
- 1 -> Least Squares Gradient Descent
- 2 -> Least Squares Stochstic Gradient Descent
- 3 -> Least Squares
- 4 -> Ridge Regression
- 5 -> Logistic Regression
- 6 -> Regularized Logistic Regression
If no number is specified, the default function that will be used is Least Squares.
After running the command a file called result.csv will be created in the scripts directory, and can directly be uploaded to aicrowd.com to make a submission.
The different files of the scripts directory are :
graphs.ipynb: allows us to visualized the datas' distribution to then choose which feature to remove and which ones that get their log transformation added to the data.helpers_data.py: contains the functions used to separate the data in differents data sets and pre-processed it.implementations.py: contains the implementation of the 6 machine learning models asked for this project, as well as the functions used to find the best hyper-parameters for each model.proj1_helpers.py: contains helper functions to load the data from .csv files, predict label, compute score of a prediction and create a .csv file for submission.project1.ipynb: allows us to find the best parameters for each of the model, using a grid search and a k-fold cross-validation.run.py: is used to get the .csv submission file, using the best hyper-parameters found for the chosen function.