As part of a data classification challenge, I wrote two different machine learning algorithms in the files Method1.py and Method2.py. The first method is a simple logistic regression algorithm, while method two uses a k-nearest neighbors classifier. The point of the challenge was to look at consumer demographic data and payment history and predict whether they would pay their bills next month. Both of my algorithms yielded an approximately 80% accuracy when submitted to the challenge.
All data was provided by Kaggle.