This is the repository for our project 1 for the ML course in EPFL (CS-433).
The relevant data (pushed through Git LFS) can be found in the dataset folder. Included are the x_train.csv, x_test.csv, and y_train.csv files.
The provided helpers are found within the helpers.py file, while the required implementations are found in the implementations.py file. Our own helpers that we use are found in the helpers_create_data.py file.
The process of hyperparameter tuning is conducted in the Hyperparameter_tuning.ipynb notebook, for all feature settings. The run_19.ipynb and the run.ipynb notebooks contain the process for the runs for the 19-feature and all-feature settings respectively, the former of which was used for the experimentation with data processing and balancing. Its results are shown in Scores.md.
Finally, the run.ipynb file is also the best-performing system that we have developed, which is that of the all-features model (one-hot encoded) with a learning rate gamma of 0.05 and a L2 regularisation lambda of 0. The output from running this code is the best performing result on AIcrowd.