Summary

In any data analysis task, data preparation consumes most of your time. In this chapter, you learned about different data preparation options in ML Studio, starting with exploring the importance of data preparation. Then, you familiarized yourself with some of the very common data transformation tasks, such as dealing with missing values, duplicate values, concatenating rows or columns of two datasets, SQL-like joining datasets, selecting columns in a dataset, and splitting a dataset. You also learned how to apply SQL queries to transform datasets. You explored some of the advanced options of transforming a dataset by applying math functions, normalization, and feature selection.

In the next chapter, you can start applying the machine learning algorithm and, in particular, the regression algorithms that come with ML Studio.