Orange

Orange is a set of open source tools for analysis and visualization of data processing results, it’s perfect for both experts and beginners. Orange provides a large set of tools for creating interactive workflows for data analysis and visualization. From point charts, histograms, trees, to dendrograms, networks, and heat maps, Orange offers the user a variety of visualization options.

Orange is a Python library. Data mining is done using visual programming or Python scripts. Python scripts can run in a terminal window, integrated environments such as PyCharm and PythonWin, or shells such as iPython.

The benefits of Orange for machine learning and data analysis

• For everyone – beginners and professionals.
• Perform simple and complex data analysis.
• Create beautiful and interesting graphics.
• Use in data analysis lectures.
• Access external features for advanced analysis.

The best and distinctive feature of Orange is its remarkable visual effects.

This tool contains components for machine learning, add-ons for bioinformatics and text mining, and many features for data analysis. Orange consists of a Canvas interface on which the user places widgets and creates a data analysis workflow.

Widgets offer basic functions such as reading data, displaying a data table, feature selection, learning predictors, comparing learning algorithms, visualizing data items, etc. The user can interactively explore visualizations or transfer a selected subset to other widgets.

In Orange the process of data analysis (Data mining) can be developed by means of visual programming.

Orange remembers selections and offers frequently used combinations. Orange has functions for different visualizations such as scatter plots, histograms, trees, dendrograms, networks and heat maps.

Combine widgets to create a data analytics framework. There are over 100 widgets covering most standard and specialized data analysis tasks for bioinformatics.

Orange reads files in proprietary and other data formats.

Classification uses two types of objects: learners and classifiers. Learners look at class-labeled data and return a classifier.

The regression methods in Orange are very similar to classification. They are designed to intelligently analyze class-labeled data (Data mining).

Training the underlying training datasets involves predicting individual models to achieve maximum accuracy.

Models can be derived from different samples of training data or can use different learners in the same datasets.

Learners can also be varied by changing their parameter sets.