Getting Started with Orange Tool
|What is the Orange tool?
Orange is a platform built for mining and analysis on a GUI-based workflow. This signifies that you do not have to know how to code to be able to work using Orange and mine data, crunch numbers and derive insights. You can perform tasks ranging from basic visuals to data manipulations, transformations, and data mining.
|Widgets and Channels
Orange widgets are the main components in orange which provides a visual appeal to the programming environment of the orange tool. Each widget is designed to perform the dedicated tasks.
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).
You can add any widget easily by just drag and drop to the workspace or you can add a widget from the widget menu by right click on the work-space. Moreover, the user can connect to two widgets with the channel and tell orange which data needs to be passed through that channel, orange will not allow connecting two widgets that are incompatible.
|Load data in the orange
Orange can read various data formats such as Excel, CSV, etc. To load our data set we need a file widget in our workspace. With the click on a file widget, we can load datasets that are really available in orange or we also can browser our own dataset. Orange also provides a feature to fetch data from URLs.
The orange tool also provides column information as well as the data type of feature variables, although users can modify the data type if the predicted data type by the orange is not correct.
Data table widget to show the data.
Information of dataset
With the help of the data info widget, we can find the information of our data(Name of the dataset, rows, columns, features, target, and many more).
For instance, here in this case I have selected some data in the data table widget and pass that data to data info in order to get various details of the selected dataset.
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 on right side of the File widget and connect the link between File and Data Table widget. On double clicking on the 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.
|Data Distribution
Use the Data Distribution widget to get the graphical representation of the dataset values. Here I got the distribution for various features from dataset. On selecting filter based on the petal length the data is distributed properly to three different categories.
We can also use the widget of Scatter Plot for plotting for different kinds of feature pairs. In the below image Scatter Plot is plotted for the feature pair of petal length and petal width.
That’s all for the basic exploration of the Orange tool.