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Make Data Science Work for you Using Rapid Miner


Data science is a hot topic these days. And, with good reason: the ability to use data effectively can be a competitive advantage in any industry. But how do you get started? While there are many different approaches to data science, Rapid Miner is an easy-to-use tool that helps you turn your raw data into valuable insights. Let me show you how it works!

Data preparation

Data preparation is just as important than any other step in the process. It can be done with Rapid Miner in a few simple steps.

  • First, you should prepare your data by loading it into Rapid Miner and creating a new project to store it all there. Then, create a new DataSet from your imported file and add attributes from that DataSet to your project. You can also add new attributes to the DataSet if you want – this will be useful for adding an identifier for example, which would then allow you to reference different results later on in this process (like when building predictive models). Now that we have our data loaded properly into Rapid Miner, let’s move on!

  • In order to create an actual model using this dataset (which means running queries against it), we need first create a new DataFrame from those existing tables/files we created earlier - so let’s do that now...

Visual programming

RapidMiner is a visual programming language that enables you to create models quickly. Modeling is the process of creating a representation of your business problem on which you can perform data analysis. In other words, it's how you represent your data in such a way that it can be used for further analysis. RapidMiner provides several ways to create models:

  • drag-and-drop

  • point and click

  • writing code in the RapidMiner Editor window


Let’s start with a quick overview of the Auto-modeling feature. Auto-modeling allows you to create predictive models quickly by providing a selection of algorithms that can be used.

This is especially useful if you don't have much experience building machine learning models or haven't used Rapid Miner before. The algorithms include:

  • Logistic Regression (Linear)

  • Neural Network (Multilayer Perceptron)

  • Classification Tree

Rapid Miner is an easy to use data science tool

Rapid Miner is an easy to use data science tool that makes data mining and analysis much less painful. It can be used in a variety of ways, but its main purpose is to allow users to perform data analytics without having to code. This is great because it allows people who are not experienced in coding or other programming languages the ability to use Rapid Miner as their main platform for performing tasks such as:

  • Data preparation (e.g., cleansing, merging)

  • Data discovery (e.g., exploring and visualizing your data)

  • Modeling (e.g., building predictive models)


You now have a great overview of how Rapid Miner can help you make data science work for you. This is just a sample of what the software has to offer, so

For more information about putting this information to work at your organization contact Bryan at (203) 954-5121 or


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