Most experienced data scientists would agree that data processing takes most of the time when undertaking
machine learning projects. Both data pre-processing and feature engineering quality is crucial for model performance.
However, it is not typically an easy thing to do. Dealing with real data, you are likely to encounter such problems as noise,
missing values, excessive information, etc. Building a good feature vector turns out to be just as hard. In this workshop,
you will learn some simple but effective ways of handling these problems using a public Google Play Store dataset
as an example.