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Building Machine Learning Model Using Orange Is Fun

Introduction:Data has become a powerful source of earning and predict future and people will seek to utilize it even if they don’t know exactly how. Machine learning will become a usual part of programmer’s resume, data scientists will be as common as accountants. Nowadays and for the next approximately two decades, we will continue to see a major need for machine learning and data science specialists to help apply machine learning technologies to application areas where they aren't applied today.Nowadays people prefer GUI based tools instead of more coding stuff. Orange is one of the popular open source machine learning and data visualization tool for beginners. People who don’t know more about coding and willing to visualize pattern and other stuff can easily work with Orange.Why Orange:Orange is an open-source software package released under GPL that powers Python scripts with its rich compilation of mining and machine learning algorithms for data pre-processing, classification, modeling, regression, clustering and other miscellaneous functions.Orange also comes with a visual programming environment and its workbench consists of tools for importing data, dragging and dropping widgets, and links to connect different widgets for completing the workflow.Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy, and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.Getting Started:Here is how to get started with data visualization in orange.Download Orange=> Go to https://orange.biolab.si and click on Download.For Linux Users:AnacondaIf you are using python provided by Anaconda distribution, you are almost ready to go. If not, follow these steps to download Orange:conda config --add channels conda-forgeand runconda install orange3 conda install -c defaults pyqt=5 qtPIPOrange can also be installed from the Python Package Index. You may need additional system packages provided by your distribution.pip install orange3Run shortcode to verify your setup Orange successfully. Open your Python Terminal and run the following code :>>> import Orange >>> Orange.version.version '3.2.dev0+8907f35' >>>Note: If You find result shown above then you successfully setup Orange. In case you get an error like this :from Orange.data import _variable ImportError: cannot import name '_variable'Kindly Follow These Steps: Install Orange From SourceMac and Windows user can easily setup orange in their system step by step just by following the official docs of Orange:Official docs for setup OrangeAfter installation let's start working with OrangeVisualization of DataSet in Orange:A primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics.The Main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information.” -Friedman (2008)Open Orange on your system & create your own new Workflow:After you clicked on “New” in the above step, this is what you should have come up with:In this tutorial, we are going to see the steps for Visualization of DataSet in orange:Step 1: Without data, there is no existence of Machine Learning. So, In our first step we import our dataset and in this tutorial, I use example dataset available in Orange Directory. We import zoo.tab dataset in file widget:Step 2: In next step, we need data tables to view our dataset. For this, we use Data Table widget.When we double-click on the data table widget we can visualize our data in actual format.Step 3: This is the last step where we will understand our data, with the help of visualization. Orange make visualization pretty much easier. We just add one more widget and choose which format we would like to visualize our data like Scatter Plot.This completely works on the concept of neurons, data transfer from one layer to another layer when we connect data table to scatter plot widget then we find an actual representation of our data in the form of scatter plot.Closing Note:Orange is the most powerful tool used for almost any kind of analysis and visualizing dataset is fun using Orange. The default installation includes a number of machine learning, preprocessing and data visualization algorithms in 6 widget sets (data, visualize, classify, regression, evaluate and unsupervised). Additional functionalities are available as add-ons (bioinformatics, data fusion and, text-mining).Hope, this tutorial helps you to understand how to visualize data set using orange. It is very important to understand the flow of data, this helps you to figure out problems easily.Keep Practicing with OrangeHappy Coding
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Building Machine Learning Model Using Orange Is Fun

Himanshu Awasthi
Blog
19th Apr, 2018
Building Machine Learning Model Using Orange Is Fun

Introduction:

Data has become a powerful source of earning and predict future and people will seek to utilize it even if they don’t know exactly how. Machine learning will become a usual part of programmer’s resume, data scientists will be as common as accountants. Nowadays and for the next approximately two decades, we will continue to see a major need for machine learning and data science specialists to help apply machine learning technologies to application areas where they aren't applied today.

Nowadays people prefer GUI based tools instead of more coding stuff. Orange is one of the popular open source machine learning and data visualization tool for beginners. People who don’t know more about coding and willing to visualize pattern and other stuff can easily work with Orange.


Why Orange:

Orange is an open-source software package released under GPL that powers Python scripts with its rich compilation of mining and machine learning algorithms for data pre-processing, classification, modeling, regression, clustering and other miscellaneous functions.

Orange also comes with a visual programming environment and its workbench consists of tools for importing data, dragging and dropping widgets, and links to connect different widgets for completing the workflow.

Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy, and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.


Getting Started:

Here is how to get started with data visualization in orange.
Download Orange
=> Go to https://orange.biolab.si and click on Download.

Linux Users:
For Linux Users:

Anaconda
If you are using python provided by Anaconda distribution, you are almost ready to go. If not, follow these steps to download Orange:

conda config --add channels conda-forge

and run

conda install orange3 conda install -c defaults pyqt=5 qt


PIP
Orange can also be installed from the Python Package Index. You may need additional system packages provided by your distribution.

pip install orange3

Run shortcode to verify your setup Orange successfully. Open your Python Terminal and run the following code :

>>> import Orange
>>> Orange.version.version
'3.2.dev0+8907f35'
>>>

Note: If You find result shown above then you successfully setup Orange. In case you get an error like this :

from Orange.data import _variable
ImportError: cannot import name '_variable'

Kindly Follow These Steps: Install Orange From Source

Mac and Windows user can easily setup orange in their system step by step just by following the official docs of Orange:
Official docs for setup Orange
After installation let's start working with Orange


Visualization of DataSet in Orange:

A primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics.

The Main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information.” -Friedman (2008)

Open Orange on your system & create your own new Workflow:


After you clicked on “New” in the above step, this is what you should have come up with:

Visualization of DataSet in orange:

In this tutorial, we are going to see the steps for Visualization of DataSet in orange:

Step 1: Without data, there is no existence of Machine Learning. So, In our first step we import our dataset and in this tutorial, I use example dataset available in Orange Directory. We import zoo.tab dataset in file widget:

Step 2: In next step, we need data tables to view our dataset. For this, we use Data Table widget.

visualize our data in actual format.
When we double-click on the data table widget we can visualize our data in actual format.


Step 3: This is the last step where we will understand our data, with the help of visualization. Orange make visualization pretty much easier. We just add one more widget and choose which format we would like to visualize our data like Scatter Plot.


This completely works on the concept of neurons, data transfer from one layer to another layer when we connect data table to scatter plot widget then we find an actual representation of our data in the form of scatter plot.



Closing Note:

Orange is the most powerful tool used for almost any kind of analysis and visualizing dataset is fun using Orange. The default installation includes a number of machine learning, preprocessing and data visualization algorithms in 6 widget sets (data, visualize, classify, regression, evaluate and unsupervised). Additional functionalities are available as add-ons (bioinformatics, data fusion and, text-mining).

Hope, this tutorial helps you to understand how to visualize data set using orange. It is very important to understand the flow of data, this helps you to figure out problems easily.

Keep Practicing with Orange

Happy Coding

Himanshu

Himanshu Awasthi

Blog author
I'm final year computer science enthusiast ,Foss lover, speaker and technical blogger. I'm organiser of KanpurPython, PyData Kanpur & co founder of KanpurFoss Community.

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Top comments

sherif

March, 20th at 12:58pm
Can i use orange with R tool what i mean can i visualize my result from R using orange

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