cs-433 / ml-project-1-ml_pls2021

ml-project-1-ml_pls2021 created by GitHub Classroom

ml-project-1-ml_pls2021

ml-project-1-ml_pls2021 created by GitHub Classroom

General Information

The repository contains the code for Machine Learning course 2021 (CS-433) project 1.

Team

The project is accomplished by team PLSteam with members:

Leandre Castagna: @Defteggg
Pascal Epple : @epplepascal 
Selima Jaoua : @salimajaoua

Project structure

Presentation :

The data can be found on the GitHub of the course : https://github.com/ML_course/blob/master/projects/project1/data. To run our code, please download the data and put it in the same folder as our files.
proj1_helpers : we changed the function predict_label in two ways : first, we modified the prediction from -1,1 to 0,1. Also, we added an input variable which tells us if the method is logistic or not. Depending on the method, the prediction function will change.

Data analysis :

dataAnalysis.py : process data for model by splitting the classes, delete missing values, and multiply features depending on a certain threshold.

Methods :

implementations.py : the implementation of 6 methods to train the model.

cross_valisation.py : use cross-validation to find the best parameters for ridge regression.

Folder Mains : for each method, you can find a Jupyter Notebook file that outputs the prediction for both train and test data. To run this, please download this and place them in the same folder as both the data and implementations.py

Best model :

run.py /run.ipynb : Results using the best model ( Regularized Logistic Regression ) for both train and test (We perfer to work with a Jupyter Notebook for the submissions)
finalsubmission.csv : Prediction for the test data with our best model

Report

report.pdf: a 2-pages report of the complete solution.