Getting Started With Orange Tool

Orange Tool

Orange is a free and open-source toolkit for data visualization, machine learning, and data mining. It comes with a visual programming front end for exploratory qualitative data analysis and interactive data visualization.

Widgets are orange components that span from simple data presentation, subset selection, and preprocessing to empirical evaluation and predictive modelling of learning algorithms.

Advanced users can use Orange as a Python library for data manipulation, while visual programming is implemented through an interface in which workflows are formed by linking predefined or user-designed widgets.

How to use workflows in Orange

Components that read, process, and visualize data make up Orange Workflows. Widgets communicate by sending data along with a channel for communication. One widget's output is utilized as the input for another. This results in a workflow.

Let's start by putting together a basic workflow for any dataset. You can use one of the inbuilt datasets provided by Orange or import one of your own. Here, I used the inbuilt dataset iris. This is the step-1 of simple workflow



Step-2: We use the File widget to import the dataset, and then we establish a flow between File-Data Info, File-Data Table, File-Distributions, and File-Scatter Plot. We can design a simple workflow in Orange this manner.



Data Information

The Data Info widget is used to acquire information about the loaded data. It displays the dataset's name, size, features, description, row count, column count, targets, and data characteristics, as well as the dataset's row count, column count, and targets.


Use the Data Table widget to view your data in tabular format. Simply drag and drop the widget into the canvas and make a link from the File widget to the Data Table widget.



Data Distribution

The data Distribution widget is used to get a graphical representation of the dataset values. Here one can easily view distribution for different features from the dataset. In the below snapshot, you can observe the distribution based on sepal length. Similarly, you can view distributions for sepal width, petal length, petal width, etc.


Another one, the Scatter Plot widget to plot different kinds of feature pairs.

That’s it! You now have a basic understanding of the Orange tool.

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