Step 12: Run the software, initialisation of files will occur. Step 11: RapidMiner is successfully installed on the system and an icon is created on the desktop. Step 10: Click on Finish to finish the installation process. Step 9: After this installation process will start and will hardly take a minute to complete the installation. Step 8: The next screen will be of installing location so choose the drive which will have sufficient memory space for installation. Step 7: The next screen will be of License Agreement, click on I Agree. Step 6: After this Setup screen will appear, click on Next.
Step 5: It will prompt confirmation to make changes to your system. Step 4: Now check for the executable file in downloads in your system and run it. It is a big 273 MB file that will take some time depending on download speed.
Step 3: New webpage will open, which will contain RapidMiner installation files for the different operating systems, let’s take a 64 bit Windows installer file, click on it, downloading the executable file will start shortly. Click on the Downloads button which is adjacent to My Account, no need to create an account. Step 2: Clicking on the DOWNLOAD button will redirect to another webpage. Step 1: Visit the official website of RapidMiner using URL on any web browser.
Installing RapidMiner on Windowsįollow the below steps to install RapidMiner on Windows: More than 700 companies use RapidMiner for different data science projects. It is available in both free and paid versions. It is a cross-platform application and can be used on different operating systems like windows, Linux, etc. The latest version of RapidMiner is 9.10.0 which was released on 12 August 2021. Main developers of RapidMiner are Ralf Klinkenberg, Ingo Mierswa, and Simon Fischer. Earlier it was known as YALE (Yet Another Learning Environment). It is built by using the Java programming language. It is developed by RapidMiner Incorporation in 2001.
RapidMiner is software used in the field of data science for different purposes like machine learning, sentiment analysis, text mining, data cleaning, data mining, etc.
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