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SPSS Modeler 18 - Professional Edition versions & prices
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The Professional edition of IBM SPSS Modeler is, thanks to its additional server functionality, an ideal scalable solution for a whole department or teams of any size. Moreover, the Professional edition provides additional profitable features such as: Internal database mining, SQL pushback, Analytic Server for Hadoop/Spark connectivity and more. The IBM SPSS Modeler Professional contains advanced algorithms, data manipulation and automated modeling and preparation techniques to build predictive models that can help entire departments deliver better business outcomes.

IBM SPSS Modeler
Predictive Analytics in in a single solution

Today predictive analytics is a central key component in any modern IT-department of a company. Using predictive analytics to analyse and predict complex economic relationships gives your company a huge advantage and provides management with optimal operational decisions. Companies will be able to plan their decisions and strategies more effectively, predict risks more precisely and so on. IBM SPSS Predictive Analytics product line features broad and deep descriptive and predictive analytics, data preparation and automation and provides analytics of structured and unstructured data from virtually any source. The IBM SPSS Modeler is an extensive predictive analytics plattform, that brings predictive intelligence to individuals, groups, systems and companies for their decision making. The Modeler offers numerous advanced algorithms and procedures, including text analysis, entity analytics, decision management and optimization. Thus you can consistently make the right decisions - from the desktop or within operational systems.


Your Benefits

Insights from all your
Data

Analyse structured and unstructured data in files, relational databases, Hadoop distributions and more. You can combine, transform and visualize your data in an intuitive way and prepare them for the analysis.

Deep Machine
Learning

IBM SPSS Modeler offers a wide range of analysis functions among them machine learning, automated modelling, ensemble modelling, simulation, geo spatial analysis and Big-Data algorithms.

Flexible implementation options

Benefit from easy and clear reports and flexible implementation options like connect to Business-Intellegence, batch- and real time scoring, analytical decision management on site, in the cloud or in a hybrid model.

Extensibility and Programmability

Scripting languages like Python and R can be used for native function expansion. Create new features and functions for Business Analysts with R and use free extensive applications made by the IBM Predictive Analytics Community.

Available Editions

IBM SPSS Modeler is available in different editions with adjusted fetaures to fit your needs. The following editions are available:

Editions Features
Personal The most compact solution. Design and creation of predictive models that reveal patterns and trends in structured data.
Professional   Provides additional options for internal database mining, SQL pushback, cooperation and implementation, Champion/Challanger-A/B testing and more.
Premium Additional features for the analysis of unstructured data, text in natural language and social networks.

User Interface

Further Information

Downloads for the software IBM SPSS Modeler


Data Sheet | Helpful information about the IBM SPSS Modeler its applications and functions: read Data Sheet

White Paper | Use time and location-based intelligence to reveal hidden insights about your business, customers or constituents: read White Paper

Big Data with IBM SPSS Modeler | This document presents how to use IBM SPSS Modeler as an efficient source for accurate predictions: read Paper

Installation Instructions


Desktop Installation

Server Installation

System Requirements for the Software IBM SPSS Modeler

Details of the installation can be taken from the appropriate installation instroduction (see the tab 'Downloads').

Desktop Systems

  Windows® Mac® OS X
Additional Requirements Min. display resolution: 1024x768
Operating System Windows 7, 8, 8.1, Windows 10 (32-/64-Bit) Mac OS X 10.10 (Yosemite), 10.11 (El Capitan) (only 64-Bit!)
Min. CPU Intel® Pentium® or Pentium-class processor, or higher (for 32-Bit Windows)
x64 (AMD 64 and EM64T) processor family (for 64-Bit Windows)
Min. RAM 4 GB RAM
Disk Space 20 GB free disk space

Server Systems

  Windows® Server Linux® Server
Additional Requirements Min. display resolution: 1024x768 Linux (64 bit) kernel 2.6.28-238.e15 or higher
FORTRAN version libgfortran.so.3
C++ Version libstdc++.so.6.0.10
Operating System Windows Server 2008 or 2012 (only 64-Bit!), Red Hat® Enterprise Linux 6, 7, 7.1 (only 64-Bit!)
SUSE Linux Enterprise Server 11 (only 64-Bit!)
Ubuntu 14.10 (only 64-Bit!)
Min. CPU UNIX Hardware: PowerPC processor, 233MHz or better and IBM System p for IBM AIX
UltraSPARC II or better for Solaris
x64 (AMD 64 and EM64T) processor family or IBM System z for Linux 64-Bit
Min. RAM 4 GB RAM
Disk Space 20 GB free disk space

New functions in IBM SPSS Modeler 18

  • Modeler Personal and Modeler Professional now running under MAC OS. (Modeler Premium will be added later!)

Big Data Algorithms

Some new algorithms have been added in previous versions (17.1) to the Modeler, however these could only be carried out in combination with the Analytic Server. In version 18 now all of these algorithms can be used directly in the Modeler without Analytic Server. There is also an improved time series algorithm. All these algorithms support parallel processing for the model building (model building will be done much faster - Big Data algorithms).
The following algorithms from version 17.1 are now available in Modeler 18 (without Analytic Server):

  • Statistical methods: Linear-AS and GLE
  • Linear Support Vector Machines
  • Decision trees: Random Trees and Tree-AS (i.e. CHAID)
  • Clustering algorithms: Two-Step-AS

Your benefits from the new algorithms: Multi-Threading

  • Faster modeling with Big Data through parallel processing and more efficient use of hardware ressources
  • All new algorithms are multithreaded even in the local Modeler (without Modeler Server, resp. Analytic Server)
  • In previous versions of the Modeler, multithreaded algorithms relied on one of the above mentioned server

Your benefits from the new algorithms: Regularization

  • Prevents “Overfitting” (inaccurate predictions of new data) by adjusting extreme and complexe parameters
  • Building models without regularization often leads to excellent results just for the data on which the model was constructed, but not for new data.
  • Available in GLE and Linear Support Vector Machines

Your benefits from the new algorithms: Automated Data Preperation

  • Tree-AS and Linear Support Vector Machines preparing data automaticaly in the background
  • Automated Data Preperation drasticaly reduces the amount of work you have to do as well as the error rate of a manuall data preperation
  • Just three short examples for this feature: categorial fields with more than 12 occurrences will be merged (Default: <=12Bins), transformation of a date, resp. time field into a continious variable (i.e. birthday to age), empty spaces in string fields are 'trimmed'

Overview: Random Trees

  • Random Trees are 'Ensemble Models', based on a diversity of decition trees (C&RT)
  • The main goal of Random Trees are exact predictions of depending variables. So the identification of unknown correlations and patterns are not the focus.

Overview: New Time Series Algorithm

  • 'Split' Modeling - different time series predictions are calculated for defined groups (using the split variable). Multithreaded and runnable in Analytic Server.
  • If f.e. the split variable is the gender or the different stores of a retailer, then you can create forecasts based on time series.

Open Source: Python for Spark, Predictive Extensions

The IBM SPSS Modeler is capable of solely running Python for Spark (no Analytic Server needed). On-demand integration of Predictive Extensions are much more simplified. This shows that SPSS continues the way to integrate open source technologies in their system (see also R).

  • Spark is an open source technology and very fast in the context of Big Data Analytics. Thanks to the in-memory technology of Spark it is actualy much faster than similiar techniques.
  • Spark MLlib algorithms are accessable through Python for Spark and even for non-Hadoop data sources. Collaborative Filtering and Page Rank extensions are already available
  • The Custom Dialog Builder now supports Python for Spark even without Analytic Server. The progrmmed code will be integrated into userfriendly GUIs and therefore enables the access to Spark functions even for 'non-programmer'.
  • Predictive Extensions can be loaded within the Modeler directly so that algorithms can be used without any detours.

SPSS Community

  • New in Modeler 18: direct access to forums and the SPSS community
  • The SPSS community is THE central contact point of SPSS users
  • All technical infos & support, even Predictive Extensions
  • Support via chat or e-mail, without a purchase of a license/ ICN/ Recorded Entitlement
  • New DB2 on Z/OS In-Database algorithms. Five In-Database algorithms running in DB2 on Z/OS or IDAA (IBM DB2 Analytics Accelerator): Decision Tree, Regression Tree, K-Means, Naïve Bayes, Two-step