What is RapidMiner?

In this article, we will explore the architecture of the RapidMiner tool and learn the step-by-step approach to using the tool to build machine learning models.

Table of Content

1. What is RapidMiner?
2. The Architecture Of RapidMiner
3. Step-By-Step Guide To Using RapidMiner
4. Conclusions

What is RapidMiner?

RapidMiner is a platform for data scientists and big data analysts to quickly analyze their data. RapidMiner has made a big leap in the AI community as it is most popularly used by non-programmers and researchers. The platform offers a large number of options in terms of plugins and data analysis techniques. In addition, it is compatible with iOS, Android and Web application tools such as Node JS and Flask. This platform is useful for people who have an idea and want to experiment without spending much time or effort on it.

The architecture of RapidMiner

AIM Daily XO

The idea behind the RapidMining tool is to create a place for everything. You can do it all here by starting with providing multiple datasets for model deployment through the platform. Some of the features of this platform are

1. Rapidminer offers its own collection of datasets, but it also offers the option to set up a database in the cloud to store large amounts of data. You can store and load data from Hadoop, Cloud, RDBMS, NoSQL and so on. In addition, you can load your CSV data very easily and start using it as well.
2. Standard implementation of processes like data cleaning, visualization, pre-processing can be done with drag and drop options without writing a single line of code.
3. RapidMiner also offers a wide range of machine learning algorithms in classification, clustering and regression. You can also train optimal deep learning algorithms like Gradient Boost, XGBoost and so on. Not only that, the tool also offers the ability to prune and tune.
4. Finally, to tie everything together, you can easily deploy your machine learning models on web or mobile through this platform. You only need to create the user interface to collect real-time data and run on the trained model to complete a task.

Step-by-step guide to using RapidMiner

1. The first step is to download the RapidMiner tool to your local system. You can click here to download the tool. Download the 'RapidMiner Studio' option and select the operating system type of your system. Once done, wait for the download to complete and set up your account in Studio.
2. After creating your account, you will see this screen. Depending on your requirements you can choose whichever template you want to use. Since this article is concerned with building and implementing machine learning models, I will opt for the Turbo Prep option. To load some data, click the green button. Then, click Sample Folder->Data. Once you have navigated to this folder you can see a list of datasets. I have chosen Iris dataset. You can load your own dataset either from your local system or from the database by clicking the Import Data option.
3. For visualization purposes of the data, you can click on the results button, drag and drop your dataset and you will be able to see some options as shown below. Click the Visualization button on the left. Here you can play with the data visualization and see how the numbers relate to each other. There are lots of visualization types available as shown below.
4. As you can see there are a few options for doing data processing. You can transform data, clean it, generate new data, analyze statistics using pivots or merge columns together. Let us now explore these options. The pivot option helps to perform statistical analysis. You can drag and drop columns to group them with targets. Once we have grouped the columns that we need to analyze, we can select options like Total, Average, Median, etc. to achieve our desired result.
5. Once the data is cleared, we can begin the modeling process. Select the option of auto-model and select the dataset that has just been processed. You will be presented with options such as prediction, outlier or cluster identification. Since the iris dataset is mostly used for prediction, I will select the prediction option and select my target column. Next, select the models you want to experiment with. If you are unsure which model will perform better you can select all the models and then compare the performance. You also have the option to choose where the execution should take place. You can execute on local systems or cloud.

Conclusions

The purpose of this article is to demonstrate how to make good use of the RapidMiner tool for researchers and non-programmers to be able to experiment with data science RapidMiner makes the machine learning process very reliable, easy and efficient to use. As you saw we have managed to train 203 models without writing a single line of code.