People often borrow ideas and apply them their situations. Start borrowing some great processes and use them for your own use case.
Author: Thomas Ott
Data Prep series part 5: Outlier Detection. How to detect outliers and determine whether they are important or erroneous data that needs correction.
Feature Generation and Selection is the next step on transforming your data and we have some handy operators to help you make this process fast and easy.
After upgrading RapidMiner Studio, you might be wondering where your processes went. No need to worry, we’ve got you covered!
As Data Scientists, Engineers and Analysts, you have to routinely transform data from one type to another. RapidMiner makes converting data types easy.
Data quality refers to the right type of data being in the right place. Learn how to improve the quality of your data by replacing missing values.
You must spend time on data exploration; you must think about the problem you’re trying to solve, bring the right data together and then inspect it.
Easily access Federal Reserve Economic Data or FRED API data in RapidMiner Studio using only two operators: Open File and Read XML.
Take advantage of building blocks, pre-built processes encapsulated inside a Subprocess meant to help speed up your analytics.
Learn how to configure Studio settings and add proper path properties to execute R and Python scripts in RapidMiner Server.