Other posts in this series: Bank Marketing - Part 1 - Making the call - An Exploratory Data Analysis Bank Marketing - Part 2 - Who will say yes? - Building Classification Models In Part 1, we found that the subscription rate changed a lot between 2008 and 2010, as interest rates fell and the bank changed its campaigns. In Part 2, we built models on a random sample of the calls and tested them on the other calls from the same period. They did well. But when we trained them on the earlier calls and tested them on the later calls, they did much worse.
Other posts in this series: Bank Marketing - Part 1 - Making the call - An Exploratory Data Analysis Bank Marketing - Part 3 - Keeping up with the times - Sliding and Expanding Window Models In Part 1, we explored the Bank Marketing dataset: 41,188 calls in which a Portuguese bank asked its clients to open a term deposit. Only about 1 in 9 clients said yes.
In this post, we build models that predict whether a client will subscribe. A model is a formula that a computer learns from examples. A classification model does not just answer yes or no: it gives each client a probability of saying yes. A bank can use these probabilities to call the most promising clients first.
Other posts in this series: Ames Housing - Part 1 - Laying the foundation - Building a Data Pipeline Ames Housing - Part 2 - Walkthrough - An Exploratory Data Analysis In Part 1, we built a data pipeline that cleans the Ames Housing dataset, and in Part 2, we explored the finalized dataset.
In this post, we build linear and non-linear models, and we find out how well they predict the SalePrice of properties. A model is a formula that a computer learns from examples. A linear model adds up the features, each multiplied by a number (a coefficient) that it learns from the data. A non-linear model can also learn curved relationships.
Other posts in this series: Diamonds - Part 1 - In the rough - An Exploratory Data Analysis Diamonds - Part 2 - A cut above - Building Linear Models In the previous posts, we found out which attributes of a diamond are important for its price. An exploratory data analysis showed that carat, clarity and color are the most important features to predict price. Then we built linear models to predict the prices, and we chose the best model by its metrics.