This is the source code for our 3rd and final project in the course FYS-STK4155 Applied Data Analysis and Machine Learning at the University of Oslo.
This project aims at comparing the classification performance of a Multilayer Perceptron model and an XGBoost model with the much simpler method of k Nearest Neighbours. The data set used can be found here.
The code in this project is relatively short and is therefore contained in a single Jupyter notebook.
Please install dependencies using Pipenv by using the command pipenv install.
- src/main.ipynb: Main notebook containing all code used.
- src/data: Folder containing the data set.
- src/models : Folder containing the models.
- doc/report_3.pdf: Project report.
- doc/report_3.tex: Report latex file.
- doc/figures/: Folder containing all figures generated and used in the report.
We have used Black for proper code formatting in Python.