This is the code developed to complete Georgia Tech CS 7641 (Machine Learning) assignment 1 (Supervised Learning).

The objective was to fit two datasets:

• A subset of Google's Quick, Draw! dataset here
• Credit card fraud detection using a dataset from Kaggle here

Using five classification algorithms:

• Decision Trees
• Support Vector Machines
• Artificial Neural Networks
• Boosting (using AdaBoost)
• K-Nearest Neighbors

The code is broken up into a Jupyter Notebook for each data-algorithm combination, each of which contains a few notes describing results. Supporting helper functions are contained in utilities.py, and data in the data folder (note that the data is compressed and must be uncompressed to run code).