"Hello, Data!" - Understand the world through data

When banks and other financial institutions evaluate a loan request, they must ascertain the risk of default. Defaults can be very costly to banks and predictive analytics can be deployed to help banks make the right decisions. This project involves using a past Kaggle competition to model default of a customer based on many different features. Data Exploration The dataset comprises over 300,000 customers with over 100 features; it is quite a large dataset packed with data. Let's try to make sense of it. It gives demographic and financial information of a customer and then says if he/she defaulted on a loan. Let's first get a broad-level overview of the fraction of defaults. The bank issues both cash loans and revolving loans. The latter are flexible loans and are rather open-ended. Let us see how default rates vary based on the type of loan. It seems that a smaller proportion of revolving loans,

I know nothing about music. This makes it ideal as a topic for me to study and apply data without any (informed) misconceptions. This exploration is really just an expansion of what a team of two other students and I did for a Data Competition at the Fuqua School of Business. The competition involved creating the dataset. For this pet project, I also created some analysis. Acknowledgements I want to thank the Master of Quantitative Management team at the Fuqua School of Business and the Fuqua School of Business as a whole for giving us the opportunity to participate in this competition and further build our data collection and analysis skills. I would also like to thank my teammates in the competition, without whom the dataset which I have analyzed further would not exist. Data The data comprises the top 20 songs and their respective artists from the year-end US Billboard Hot 100 for the years 2006 to 2018. The data was scraped from the US Billboard website. Song metrics were ob