Comp472-Assignment1 GitHub URL:

How to run the program:

Note: The program was created using Jupyter Notebook, and each part (model) has been separated into it's own unique .ipynb file.

To ensure Jupyter is installed, open a command-line window and run the following command: conda install jupyter
To start the Jupyter Notebook instance, open a command-line window within the project's root directory.

Type in the command: jupyter notebook and wait for the notebook to open in your default browser.

Click on the "Run" button at the top of the notebook to run the entire section of code.

To view the output .csv files, navigate to the /results directory and select any of the specific files. There is one subdirectory for each of the models (a through f) as to keep the folder organized. The training and testing sets' distribution graphs (images) can also be found within the /results directory as well.

Implementations for the sections "(a) GNB and (b) Base-DT" were done by Andrew K. (40055081)
Implementations for the sections "(c) Best-DT and (d) PER" were done by Janghuk B. (40005573)
Implementations for the sections "(e) Base-MLP and (f) Best-MLP" were done by Jixuan L. (40073785)

Name		Name	Last commit message	Last commit date
Latest commit History 24 Commits
data-sets		data-sets
images		images
results		results
.DS_Store		.DS_Store
.gitignore		.gitignore
Base_MLP.ipynb		Base_MLP.ipynb
Best-DT.ipynb		Best-DT.ipynb
Best_MLP.ipynb		Best_MLP.ipynb
Demo-Slides.pptx		Demo-Slides.pptx
README.md		README.md
base-decision-tree.ipynb		base-decision-tree.ipynb
naive-bayes.ipynb		naive-bayes.ipynb
perceptron.ipynb		perceptron.ipynb