Orange Data mining
Orange is an open source data visualization and analysis tool, where data mining is done through visual programming or Python scripting. The tool has components for machine learning, add-ons for bioinformatics and text mining and it is packed with features for data analytics.
Data Mining Software
Open Source Software
Orange Data mining : Orange is an open source data visualization and analysis tool. Orange is developed at the Bioinformatics Laboratory at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, along with open source community. Data mining is done through visual programming or Python scripting. The tool has components for machine learning, add-ons for bioinformatics and text mining and it is packed with features for data analytics. Orange is a Python library. Python scripts can run in a terminal window, integrated environments like PyCharm and PythonWin, or shells like iPython.
Orange Data mining
In Orange, data analysis process can be designed through visual programming. Orange remembers the choices, suggests most frequently used combinations. Orange has features for different visualizations, such as scatterplots, bar charts, trees, to dendrograms, networks and heatmaps. By combining the various widgets the design of data analytics framework can be done. There are over 100 widgets with coverage of most of standard data analysis tasks and specialized add-ons for Bioorange for bioinformatics.
Own widgets, can be developed and the scripting interface can be extended to create self contained add-ons, integrating with the rest of Orange, allowing components and code reuse. Orange runs on Windows, Mac OS X, and variety of Linux operating systems.
Orange comes with mutliple classification and regression algorithms. New ones can be build or there are features to wrap existing learners and add some preprocessing to construct new variants.
Orange can read files in native and other data formats. Orange is devoted to machine learning methods for classification, or supervised data mining. Classification uses two types of objects: learners and classifiers. Learners consider class-labeled data and return a classifier. Regression methods in Orange are very similar to classification. Both intended for supervised data mining, they require class-labeled data. Learning of ensembles combines the predictions of separate models to gain in accuracy. The models may come from different training data samples, or may use different learners on the same data sets. Learners may also be diversified by changing their parameter sets. In Orange, ensembles are simply wrappers around learners. They behave just like any other learner. Given the data, they return models that can predict the outcome for any data instance.Orange
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.