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RapidMiner

A fully transparent, end-to-end data science platform
our rating 4.5 out of 5 Stars

RapidMinerProduct Overview

  1. About RapidMiner
  2. Pros of RapidMiner
  3. Cons of RapidMiner
  4. Breakdown of core features

RapidMiner product overview

RapidMiner is a cloud-based and on-premise data science platform built to help analytics professionals unify the entire data science lifecycle. The platform accelerates the building of complete analytical workflows, from data prep to machine learning to model validation to deployment, in a single environment

RapidMiner comes with data integration, transformation, machine learning, and application integration. The platform expedites learning, improves standardization, and simplifies maintenance and extensibility, aiming to boost productivity and efficiency.

It helps organizations access, load, and analyze structured and unstructured data. It provides a visual drag-and-drop tool and machine learning library, which enables developers to design, create, and deploy predictive models. The system simplifies data access, allowing analytics teams to access, load, and evaluate all types of data, including texts, images, and audio tracks.

RapidMiner is a centralized solution that features a robust graphical user interface that enables users to create, deliver, and maintain predictive analytics. Aside from allowing users to create advanced workflows, RapidMiner provides rich technology that can be useful when working in the various stages of an advanced analytic project.

Pros of RapidMiner

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  • The platform provides total transparency, from data ingestion through deployment and optimization.
  • RapidMiner’s project-based collaboration framework shows all changes and who executed them. Every step in the process is visualized and can be inspected and edited.

Cons of RapidMiner

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  • Because of the complex nature of the tool, RapidMiner’s learning curve can be steep for those with little experience.

Breakdown of core features

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RapidMiner offers features for visual workflow design and full automation.

Visual workflow designer

Analytics teams can speed up and automate the creation of predictive models using the platform’s drag-and-drop visual interface. RapidMiner has a library with over 1,500 algorithms and functions and pre-built templates for common use cases including customer churn, predictive maintenance, and fraud detection. RapidMiner also has a Wisdom of Crowds functionality that provides proactive recommendations at every step to help beginners.

Automated in-database processing

With this feature, analytics professionals can run data prep and ETL inside databases to keep the data optimized for advanced analytics. The platform allows query and data to be retrieved without writing complex SQL. It allows teams to harness the power of highly scalable database clusters, too. RapidMiner supports MySQL, PostgreSQL, and Google BigQuery.

Data visualization and exploration

Users can evaluate data health, completeness, and quality with RapidMiner. Analytics professionals can understand patterns, trends, and distributions with the platform’s scatter plots, histograms, line charts, parallel coordinates, and box plots. The solution enables analytics teams to find and fix common data quality problems, including missing values and outliers. Users can also explore data using RapidMiner’s statistical overviews and interactive visualizations.

Data prep and blending

RapidMiner eliminates the hassle of preparing data for predictive modeling. The RapidMiner Turbo Prep feature offers a fully interactive point and click data prep experience to let users extract, join, filter, and group data across any number of sources. Analytics teams can create repeatable data prep and ETL processes that can be scheduled and shared.

(Last updated on 04/15/2021 by Liz Laurente-Ticong)

Quick Facts

  • Industry Specialties
    All Industries
  • Pricing
    High-end
  • Works Best For
    Any Sized Businesses

Screenshots

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Features

  • Visual Workflow Designer
  • Data Access and Management
  • Data Exploration
  • Descriptive Statistics
  • Graphs and Visualization
  • Data Prep
  • Data Sampling
  • Data Partitioning
  • Data Replacement
  • Weighting and Selection
  • Similarity Calculation
  • Clustering
  • Market Basket Analysis
  • Bayesian Modeling
  • Modeling Evaluation
  • Scoring
  • Automation and Process Control

Integrations

  • Tableau
  • MonkeyLearn
  • TAS Insight Engine
  • MeaningCloud Text Analytics
  • Datalytics

Pricing Model

  • RapidMiner Go
  • RapidMiner Studio Free
  • RapidMiner Studio Enterprise

Languages

  • English
  • Japanese