Data Science: Orange Tool Basic Overview
What Is Orange Tool?
Orange is a data visualization, machine learning, and data mining toolkit that is open-source. It comes with a visual programming front-end for exploratory data analysis and interactive data visualization, as well as the ability to utilize it as a Python library. Orange is a component-based software suite that excels at machine learning and data mining, specifically visualization. Widgets are the components that cover everything from visualization to pre-processing, evaluation, and predictive modelling.
- Unsupervised and so on.
Widgets offer essential functionality, like:
- Displaying data table and allowing to selection features
- Data reading
- Training predictors and comparison of learning algorithms
- Data element visualization, etc.
There are 3 ways to add widget to the canvas:
- Double click on the widget.
- Drag the widget to the canvas.
- Right click on the canvas for the widget menu.
The Workflows of Orange tool:
Orange Workflows are made up of components also known as widgets that read, analyze, and visualize data. Orange widgets are data analysis workflow building elements that are assembled in Orange’s visual programming environment. Widgets are classified into classes based on their purpose. A typical workflow may mix widgets for data input and filtering, visualization, and predictive data mining.
Now, let’s generate a workflow for the well known IRIS dataset. You can either use one of Orange’s built-in datasets or import one of your own.
Step-1: Using widgets library select File widget
Widget options in the left pane of orange window
Now, after double clicking the File, we need to select the dataset, here we have utilized the Heart-disease dataset.
Step-2: Create links from File to Data Info, Data Table, Distributions, and Scatter Plot.
How to do basic data exploration (like data distribution, data information):
To get information about the loaded data we use the Data Info widget. It shows the dataset name, size, features, description, row count, column count, and targets, and data attributes in the dataset.
To view your data in tabular form, use the Data Table widget, drag and drop the widget to the canvas and create a link from the File widget to the Data Table widget.
To acquire a graphical representation of the dataset values, utilize the data Distribution widget. The distribution of different features from the dataset may be easily viewed here. The distribution based on petal width Split by Iris can be seen in the image below. Similarly, you can use different combinations between features and target variables to visualize your datasets.
We have explored orange tool in detail and visualized the dataset that we have loaded. We tried out Distribution, and Scatter Plot widgets using the Heart-Disease dataset.