Chapter 5. Data Preparation
In practical scenarios, most of the time you would find that the data available for predictive analysis is not fit for the purpose. This is primarily because of two reasons:
- In the real world, data is always messy. It usually has lots of unwanted items, such as missing values, duplicate records, data in different formats, data scattered all around, and so on.
- Quite often, data is either required in a proper format or needs some preprocessing so that it is ready before we apply machine learning algorithms to it for predictive analysis.
So, you need to prepare your data or transform your data to make it fit for the required analysis. ML Studio comes with different options to prepare your data, and in this chapter, you will ...
Get Microsoft Azure Machine Learning now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
