Feature Engineering

In 2016, I gave a talk on the Boruta algorithm for feature selection. Unlike most feature selection procedures, Boruta aims to find all relevant features in a given dataset, meaning all features that provide some level of information. Boruta is particularly useful for the problem of aptamer selection in bioinformatics, which is quite difficult because of the highly unusual structure of the data, and because the processed data has more columns than rows.

With nothing more than latitude and longitude coordinates, you can get tons of public data and explore many interesting patterns.