Other posts in this series: Bank Marketing - Part 2 - Who will say yes? - Building Classification Models Bank Marketing - Part 3 - Keeping up with the times - Sliding and Expanding Window Models A Portuguese bank ran a series of telemarketing campaigns between May 2008 and November 2010. Its staff called clients and asked them to open a term deposit, a savings account that pays a fixed interest rate for a fixed period. The Bank Marketing dataset (Moro, Cortez and Rita, 2014) records 41,188 of these calls: who the client was, how and when they were called, the state of the economy at the time, and whether the client said yes.
Other posts in this series: Ames Housing - Part 1 - Laying the foundation - Building a Data Pipeline Ames Housing - Part 3 - Closing the deal - Building Models In Part 1, we built a data pipeline that cleans the Ames Housing dataset and creates new features. In this post, we explore the finalized dataset. An exploratory data analysis (EDA) is the first study of a dataset before we build models. We look at the features one at a time, then two and three at a time, to find which features affect the sale price. In Part 3, we use what we learn to build models.
Other posts in this series: Diamonds - Part 2 - A cut above - Building Linear Models Diamonds - Part 3 - A polished gem - Building Non-linear Models In this case study, we explore the diamonds dataset. Then, in the next posts, we build models that predict the price of a diamond. A model is a formula that a computer learns from examples. Some models are linear (they use straight-line relationships) and some are non-linear (they can also learn curves).