Introduction to Orange Data Mining Tool

Introduction to Orange Data Mining Tool

” Torturing data until it confesses …

and if you torture it enough, it will confess to anything “

– Jeff Jonas, IBM

Introduction

In this Modern Era, a term “Data” becomes fascinating and most important thing to Predict Future Information or Analysis Past Information. With the help of Data we can abstract needful Information.

As demand of Data increased, the need of Data Scientist/ Data Analyst is also increases. And one of the main reason for this Jobs in demand is high salary. But this Job Position requires Programming Knowledge to find insights from Data.

Today, I am Introducing a Tool which can able to squeeze needy information from Data without Programming. Yes, you heard right! No need to learn Programming for doing Data Science or Machine Learning Problems. Just knowledge of Algorithms and Required Data we can abstract information by using Orange Tool.

What is Orange Tool?

Orange is a component-based visual programming software package for data visualization, machine learning, data mining, and data analysis.

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.

Visual programming is implemented through an interface in which workflows are created by linking predefined or user-designed widgets, while advanced users can use Orange as a Python library for data manipulation and widget alteration.

Why Orange ?

  • Open Source
  • Interactive Data Visualization
  • Visual Programming
  • No Programming Knowledge Needed
  • Graphical Front-End for Data Analysis
  • Supports Hands-on Training and Visual Illustartions
  • Add-ons Extend Functionality

Setting-up Orange You can download Orange Tool from https://orangedatamining.com/

If you’ve already Installed Anaconda or Python to the System, you can also Install Orange by running the following snippet of code –

       For Anaconda – conda install orange3

       For Python – pip install orange3

This is what the start-up page of Orange looks like. You have options that allow you to create new projects, open recent ones or view examples and get started.

Before we delve into how Orange works, let’s define a few key terms to help us in our understanding :

  • Widget is the basic processing point of any data manipulation. It can do a number of actions based on what you choose in your widget selector on the left of the screen.
  • Workflow is the sequence of steps or actions that you take in your platform to accomplish a particular task.

There are 5 types of default widgets category given for Data Analysis :

  1. Data: widgets for data input, data filtering, sampling, imputation, feature manipulation and feature selection
  2. Visualize: widgets for common visualization (box plot, histograms, scatter plot) and multivariate visualization (mosaic display, sieve diagram)
  3. Model: a set of supervised machine learning algorithms for regression and classification
  4. Evaluate: cross-validation, sampling-based procedures, reliability estimation and scoring of prediction methods
  5. Unsupervised: unsupervised learning algorithms for clustering (k-means, hierarchical clustering) and data projection techniques (multidimensional scaling, principal component analysis, correspondence analysis)

You can add more widgets from Options → Add-ons

Conclusion

Orange is a platform that can be used for almost any kind of analysis but most importantly, for beautiful and easy visuals.

By Mansi Panchal

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