Practical 3

AIM: Explore orange tool

Theory: 

      Orange is a C++ core object and routines library that incorporates a huge variety of standard and non-standard machine learning and data mining algorithms. It is an open-source data visualization, data mining, and machine learning tool. Orange is a scriptable environment for quick prototyping of the latest algorithms and testing patterns. It is a group of python-based modules that exist in the core library. It implements some functionalities for which execution time is not essential, and that is done in Python. It incorporates a variety of tasks such as pretty-print of decision trees, bagging and boosting, attribute subset, and many more. Orange is a set of graphical widgets that utilizes strategies from the core library and orange modules and gives a decent user interface. The widget supports digital-based communication and can be gathered together into an application by a visual programming tool called an orange canvas. All these together make an orange an exclusive component-based algorithm for data mining and machine learning. Orange is proposed for both experienced users and analysts in data mining and machine learning who want to create and test their own algorithms while reusing as much of the code as possible, and for those simply entering the field who can either write short python contents for data analysis.

Explorations Task:

• Widgets: Orange widgets give us a graphical user interface to orange's data mining and machine learning techniques. They incorporate widgets for data entry and preprocessing, classification, regression, association rules and clustering a set of widgets for model assessment and visualization of assessment results, and widgets for exporting the models into PMML.
   
  

• Loading Data

• Data Info

• Correlation

• Plotting Examples