Defaulty Credit Card Data
For my most recent data science project, I decided to take a look at credit card default rates. More specifically, my "business case" was that I had been hired by a bank to help them predict credit card default. While the data was not nearly as interesting as some that I've explored earlier, the data science process itself has suddenly gotten far more exciting. Machine learning is the topic I've been waiting for since day one of Flatiron, and this first project leveraging its usefulness did not disappoint. I'd like to walk through some of the more interesting parts of the project and discuss what I'm eager to learn more about. In order to provide a good enough point of reference for the rest of the project highlight reel, I should mention a bit about the data. This was a binary classification problem, and the target was a variable called 'default' that could be zero or one (with the latter indicating that customer had defaulted). There w