Practical-3
Aim: Introduction to orange tool.
Theory:
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
2. Clicking on Data table we will be able to see all the entries in the data table of the selected dataset.
3. Then we can again draw another widget like for instance visualization widget and connect to file widget, which will help us visualize the data by showing us the graphs of all the features in the data.
4. There is also an option where you can use scatter plot directly and can visualize the data in scatter plot. It will also separate different classes on the basis of the features by ranking them.
5. Then we can connect a additional widget to scatter plot to visualize the selected data in table form.
- There are various different options available for you to try, remember if any changes are done in one widget then it will be immediately propagated through the work flow.
How to do basic data
exploration (like data distribution, data information).
Distribution:
- Data distribution help us visualize the data by selecting different features and have a look at them using different graphs.
Data info:
- It gives information about data set such as name, size(rows, columns), features(categorical, numeric), Data attributes etc.
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