This task is for educational purposes on introduction to Orange Tool
Introduction to Orange Tool
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 allows you to:
– Show a data table and select features
– Read the data
– Compare learning algorithms and train predictors
– Visualize data elements
Some important terms are:
- 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.
- Workflows: Orange workflows consist of components that read, process, and visualize data. We call them “widgets.” We place the widgets on a canvas. Widgets communicate by sending information along with a communication channel. An output from one widget is used as input to another.
The objective of Orange is to provide a platform for experiment-based selection, predictive modeling, and recommendation system. It is primarily used in bioinformatics, genomic research, biomedicine, and teaching. In education, it is used for providing better teaching methods for data mining and machine learning to students of biology, biomedicine, and informatics.
How to use workflows in Orange?
I have created a simple workflow wherein the inbuilt Iris dataset provided by Orange is being used. The workflow is such that data from the dataset is sent to the data table, to Distributions for creating a distribution and a Scatter Plot is plotted from the dataset. To create this workflow we load the dataset using the File widget, and then flow between File-Data Info, File-Data Table, File-Distributions, and File-Scatter Plot is created.
For the data to be loaded in the Canvas, select the File widget from the left pane and place it in the canvas. Double click on the File widget and select the iris.tab file.
How to do basic data exploration (like data distribution, data information).
To get the information about the data loaded in the file widget we can create a flow between the File widget and use the Data Info Widget which shows the name, description, row count, column count, features, and target values in the dataset in File widget.
Then to view the data in Orange Canvas in the table form, select the Data Table widget from the left pane, place it in the canvas and connect the link between the File and Data Table widget. On double-clicking, on the Data Table widget, the entire data can be seen in the tabular form, where Orange itself decides the Target Variable based on the data received.
Use the Data Distribution widget to get the graphical representation of the dataset values. Here I got the distribution for various features from the dataset.
We can also use the widget of Scatter Plot for plotting for different kinds of feature pairs.
How to load your data in Orange and how to load external data from API in Orange?
To load your data in Orange select the File Widget and from there in you can either select the dataset provided by Orange or else browse to the dataset file in your local machine to load the data. If you want to load external data use can select the URL option in the File widget, where one can paste the external dataset link to load the data.
Basic of Orange Tool is covered in this blog. To get more about Orange Tool, visit the next blogs.
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