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Applications

Industrial and generic solutions such as Spotfire Copilot™

31 files

  1. The Alerting Framework for Spotfire® is an extension for Spotfire that allows you to leverage your existing Spotfire analysis files to receive Alerts. It provides seamless integration with Automation Services via an Alerting Task.
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  2. This is the documentation, examples and data functions using spotfire-dsml Python package (downloadable from PyPI).
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  3. The Missing Data Navigator for Spotfire® is an interactive tool for analyzing and handling missing data. It is constructed to lead you through the typical steps of missing data analysis with the aim of providing guidance along this path.
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  4. This template provides you with a means to create a network chart to display the multi-directional relationships between the nodes in your data.
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  5. This application demonstrates aspects of implementing continuous process verification using Spotfire Statistica®.
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  6. This component is showing the integration between Spotfire® and Statistica® Data Entry data. You can find here Spotfire® applications for exploring Statistica® Data Entry data.
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  7. This analysis implements simple frequency-severity models for Operational Risk event types. This forms the basis of the Loss Distribution Approach alternative in the Basel regulations.
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  8. A natural language extension to Spotfire®.
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  9. An extensible natural language generation Spotfire dashboard implemented through a TERR data function.
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  10. This is a template for calculating various model quality diagnostic plots from probability input data. At the same time, it can serve as an example of the data function for Spotfire® built in Spotfire® Data Science - Workbench (also known as Statistica)
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  11. This template detects anomalous data points using an autoencoder algorithm. It features automated machine learning to facilitate use by business analysts. The Time Series release includes time series analysis and clustering of anomalies.
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  12. This component enables Amazon SageMaker AutoPilot integration with Spotfire.
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  13. This Spotfire template enables the user to build product recommendations for personalized marketing. This template is intended for any industry providing customers with products and services such as retail, travel, or banking.
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  14. This Spotfire template enables the user to build a wide range of Shewhart quality control charts. The chart specifications can be defined interactively or retrieved from a table. This comprehensive template is built with Statistica data function.
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  15. Leverage a copy of all MAS Statistica dashboards in Spotfire®.
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  16. This set of data functions enable you to open quickly and easily any dataset or workspace result from Statistica Enterprise metarepository in Spotfire.
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  17. This component provides computation of Weibull distribution via TERR data function (for Spotfire).
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  18. This Python toolkit contains natural language processing (NLP) functions that provide exploratory analysis of text data.
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  19. Explains model's predictions using LIME and SHAP algorithms.
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  20. This dashboard helps you identify wafer map patterns that are interesting, helps to find more examples of those patterns, and gives you the ability to train accurate machine learning models to detect those patterns in future data.
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  21. Generate normal and lognormal probability plots
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  22. Tutorial demonstrating how to put machine learning into production, but also aimed at business users who want to learn how to set up a machine learning strategy in their businesses. Spotfire Platform & Spotfire Cloud template versions are available.
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  23. Extreme Gradient Boosting or XGBoost is a supervised Machine-learning algorithm used to predict a target variable Y given a set of features - Xi
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  24. Evaluate a model's performance using Scikit learn metrics.
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  25. This template and data function return TERR environmental variables.
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