RapidMiner: An Overview in 2 Easy Points

Introduction

What is Rapidminer? Big data analysts use the platform of Rapidminer, data scientists etc., to rapidly analyze and clean data before building robust models. It does not require any form of coding or knowledge on how to use rapidminer. It is hence used by researchers, non-programmers etc., working on artificial intelligence models with logistic regression Rapidminer. Rapid Miner has a gamut of tools and options for data analytics, plugins, techniques for data analysis etc. and more, making it compatible for use with web-application, mobile apps, Android and iOS applications and tools. It is also compatible with web tools like Flask, Node js, etc. and shows no rapidminer limitations when used across platforms. 

In this article let us look at:

1. The Architecture of RapidMiner
2. A Step-by-Step Guide to Using RapidMiner

1. The Architecture of RapidMiner

Rapidminer is an effective tool and provides a one-stop-shop for non-programmers and researchers to experiment with datasets without the hassle of coding. One can use multiple datasets, deploy models, and make new datasets on its platform. Its benefits and facilities, when compared to knime vs rapidminer, Keras or TensorFlow, are

• Rapidminer works on Big Data sets that are raw data providing a collection of usable datasets that can use the cloud services to store huge amounts of data. It is compatible with datasets from Cloud, NoSQL, Hadoop, RDBMS, etc., and even accepts one’s CSV data as well.

• Procedures can be implemented with ease and without code on the rapidminer server, whereby visualization, data cleaning, pre-processing of data etc., can be executed through the drag-and-drop features of rapid miner.

• It provides a gamut of algorithms for machine learning base on their clustering, classification or regression techniques. Deep learning algorithms like XGBoost, Gradient Boost, and more can be optimally trained, tuned and pruned using Rapid Miner. 

• A choice of models in Rapidminer can be compared, deployed and executed in it deploying the trained machine learning model on mobile or web platforms. 

• One needs to create an interface for users using the rapid miner tool, and the model executes in real-time while collecting requisite data for a task.

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2. A Step-by-Step Guide to Using RapidMiner

Here’s how rapid miner works. To use Rapidminer, one has to download to the local system the rapid miner tool. One can just click here. Then one needs to ensure one chooses the option of ‘Rapid Miner Studio’. Select the type of operating system and move to the set up an account option when downloading is complete. Create an account and select the template to be used. For Ex: The Turbo Prep option for a machine learning model implementation. Use the green button to load data from the Samples folder, which has an entire list of datasets one can use. One can also use the Import option to load the system with a personal dataset for rapidminer data mining. 

Use the result button from the dataset to visualize the data, and click on the visualization button when one left-clicks. This data visualization displays how the data points correlate to each other. One has several visualization options to select from and get familiar with.

Next, explore the data processing rapidminer examples and options where one can clean, transform, generate new datasets, merge columns together or use Pivot to analyze statistically. One can use the drag-and-drop options to move columns to be analyzed to target them together and find several analysis options like the desired outcome’s average, median, aggregate values, etc. Using the clean option, one can automatically clean the dataset structure and format.

Post cleaning, one has to select a label as the target column, and Rapid Miner automatically begins analysis of the data using the correlation option between sets to provide the least important column as the highlighted column. Suppose the ID column is highlighted, then this column is automatically dropped when considering the dataset. Categorical values can also be got using the option for it. Next is to do dataset normalization and PCA. This step provides clean data that can now be used for data modelling. 

For the Rapidminer data modelling process, one has to use the auto-model option with the processed dataset. This provides options from which one can select, identify outliers, predict, clusters etc. Use the prediction set like the popular Iris dataset and select the target column, click on next and view target distribution. When target analysis is completed, one is provided with options of columns. Based on one’s needs, one should select the important columns for maximum predictive efficiency.

Also, select the model types to compare performances and select the best model. One has the option of selecting which model to execute and whether execution is to be local system based or cloud-based. On execution of the best logistic regression in the Rapidminer model, one gets comparative results and can opt to view errors, confusion matrix, accuracies etc., for the model.

Conclusion

The Rapid Miner tool can be used for executing automatic models from raw data by non-programmers, researchers etc., to build and execute data science projects. The machine learning algorithms and models used by Rapidminer are efficient, easy to implement, do not need coding and are reliable to train the models when comparing the Alteryx vs Rapidminer vs Knime systems.

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