Feature engineering is one of the most important, yet elusive, skills to master if you want to be a good data scientist. Machine learning competitions are hardly ever won with strong modeling techniques alone -- it is the combination of creative feature engineering and powerful modeling techniques that makes the difference. This tutorial will give the audience practical tips and tricks to improve the performance of machine learning algorithms. We will broadly look at feature engineering for applied machine learning, touching on subjects like: categorical vs. numerical variables, data cleaning, feature extraction, transformations, and imputation.
Currently, HJ van Veen is working as a Data scientist at Nubank.