Data preparation involves transforming raw data into a form that is more appropriate for modeling. Preparing data may be the most important part of a predictive modeling project and the most time-consuming, although it seems to be the least discussed. Instead, the focus is on machine learning algorithms, whose usage and parameterization has become quite routine. Practical data preparation requires knowledge of data cleaning, feature selection data transforms, dimensionality reduction, and more. In this crash course, you will discover how you can get started and confidently prepare data for a predictive modeling project with Python in seven days. This is a big and important post.
Jul-1-2020, 02:44:30 GMT