Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative data analysis and interactive data visualization, and can also be used as a Python library.
Orange components are called widgets and they range from simple data visualization, subset selection, and preprocessing, to empirical evaluation of learning algorithms and predictive modeling.
Orange workflows consist of components that read, process and visualize data. We call them “widgets.” We place the widgets on a drawing board (the “canvas”). Widgets communicate by sending information along with a communication channel. An output from one widget is used as input to another.
Widgets: The various components present in Orange are known as widgets and they are divided into various categories like Data, Visualize, Model, Evaluate and so on.
Load data in Orange:
Using File widget we can load data. Here i have added test.csv file. all the information bout dataset will be displayed under Info and Columns part in the Dialog box. We can also change any column’s name, type, and the role and apply changes to it.
How to do basic data exploration
The Data Table widget receives one or more datasets in its input and presents them as a spreadsheet. Data instances may be sorted by attribute values. The widget also supports the manual selection of data instances.
Data info widget displays all the information about the dataset we have loaded in file widgets like Data set name, size, features, metadata, etc.
Use the Data Distribution widget to get the graphical representation of the dataset values.
We can also use the widget of Scatter Plot for plotting for different kinds of feature pairs.
We can also modify Various properties of the graph, like color, size and shape of the points, axis titles, maximum point size, and jittering can be adjusted on the left side of the widget.