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IBM SPSS Modeler Premium Faculty Pack 17.0 Academic (Download - 12 Month License)
IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions—from the desktop or within operational systems.
By integrating predictive analytics with decision management, scoring and optimization in your organization's processes and operational systems, SPSS Modeler helps your users and systems make the right decision every time.
Automate and optimize transactional decisions by combining predictive analytics, rules and scoring to deliver recommended actions in real time. Decision management capabilities enable the integration of predictive analytics and business rules into an organization’s processes to optimize and automate high-volume decisions at the point of impact.
Use a variety of modeling approaches in a single run and then compare the results of the different modeling methods. Select which models to use in deployment, without having to run them all individually and then compare performance. Choose from three automated modeling methods: Auto Classifier, Auto Numeric and Auto Cluster.
Go beyond the analysis of structured numerical data and include information from unstructured text data, such as web activity, blog content, customer feedback, emails and social media comments. Capture key concepts, themes, sentiments and trends and ultimately improve the accuracy of your predictive models.
Identity resolution is vital in a number of fields, including customer relationship management, national security, fraud detection and prevention of money laundering. Entity analytics improves the coherence and consistency of data by resolving like entities even when the entities do not share any key values.
Social network analysis
Social network analysis examines the relationships between social entities and the implications of these relationships on an individual’s behavior. It is particularly useful for those in telecommunications and other industries concerned about attrition (or churn). By identifying groups, group leaders and whether others will be affected based on influence, predictive models can be built on an individual and enhanced with their group and social behavior data.
The modeling algorithms included in SPSS Modeler are:
- Anomaly Detection. Detect unusual records with a cluster-based algorithm.
- Apriori. Identify the frequent individual items in your transactional databases and extend them to larger item sets.
- Bayesian Networks. Estimate conditional dependencies with graphical probabilistic models that combine the principles of graph theory, probability theory, computer science and statistics.
- C&RT, C5.0, CHAID and QUEST. Generate decision trees, including interactive trees.
- CARMA. Mine for association rules with support for multiple consequents and continuous feedback for deterministic and accurate results.
- Cox regression. Calculate likely time to an event.
- Decision List. Build interactive rules.
- Factor/PCA, Feature Selection. Reduce data.
- K-Means, Kohonen, Two Step, Discriminant, Support Vector Machine (SVM). Cluster and segment data.
- KNN. Model and score nearest neighbor.
- Logistic Regression. Generate binary outcomes.
- Neural Networks. Take advantage of multilayer perceptrons with back-propagation learning and radial basis function networks.
- Regression, Linear, GenLin (GLM), Generalized Linear Mixed Models (GLMM). Model linear equations.
- Self-learning response model (SLRM). Take advantage of a Bayesian model with incremental learning.
- Sequence. Conduct order-sensitive analysis with sequential association algorithm.
- Support Vector Machine. Apply non-linear functions based on computational learning theory for efficient learning on wide datasets.
- Time-series. Generate and automatically select time-series forecasting models.
Deployment bridges the gap between analytics and action by providing results to people and processes on a schedule or in real time, and enables organizations to realize the full benefit of predictive analytics. SPSS Modeler streams can be deployed as scenarios in IBM SPSS Collaboration and Deployment Services for the purposes of model refresh, automated job scheduling, or for further use by IBM Analytical Decision Management or other predictive applications.
More reasons to use SPSS Modeler
- Analyze almost all data. Conduct analysis regardless of where the data is stored (such as in a data warehouse, a database, Hadoop or flat file) and regardless of whether it is structured (age, price, product, location) or unstructured (text, emails, social media). With its client server architecture, SPSS Modeler can push the analysis back to the source for execution, which can minimize data movement and increase performance.
- Solve a variety of business problems with an extensive range of analytics. SPSS Modeler offers analytics that range from descriptive analytics to advanced algorithms and that include automated modeling, text analytics, entity analytics, social network analysis, decision management and optimization.
- Use more easily and learn more quickly. A comprehensive platform that provides analytics is next to worthless if no one can use it. The intuitive SPSS Modeler interface appeals to a wide range of users from the non-technical business user to the statistician, data miner or data scientist because it enables them analyze data in a way that suits them. The short learning curve for SPSS Modeler enables novices and advanced users to begin uncovering results almost immediately.
- Meet your needs with flexible deployment. Choose the deployment that best suits your requirements to optimize your existing IT investment. SPSS Modeler can operate independently or in operational systems on multiple platforms on your premises or in the cloud.
Product may only be sold to an individual who is currently employed in an institution of higher education located in the United States , for educational purposes and non-commercial academic research. Limit one per customer. Non-commercial academic research which means research by an employee where (i) the results of such research are not intended primarily for the benefit of a third party, (ii) such results are made available to anyone without restriction on use, copying or further distribution, and (iii) any copy of such result is furnished for no more than the cost of reproduction.
Also to note, the employee must reside in one of the 50 US states only; territories such like sovereign military locations, PR or USVI are not included.