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How to Install Orange Data Mining Tool on Linux?

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  • Difficulty Level : Easy
  • Last Updated : 24 Feb, 2022

Orange is a powerful platform to perform data analysis and visualization, see data flow and become more productive. It provides a clean, open-source platform. It was developed by The  University of Ljubljana under the GPLv3 license.

Steps of Installation

Step 1: First of all, we will install pip and other dependencies before installing Orange Tool.

sudo apt install build-essential python3-dev python3-pip

To verify the installation, run:

pip3 –version

Checking-pip-version

Step 2: Now, install the orange tool.

pip3 install orange3

Note: This command will also install various machine learning libraries and PyQt5 that may cost you additional data. 

Using Orange Tool

Run the following command on the command-line:

python3 -m Orange.canvas

Orange-Tool-Interface

Orange Widgets

These are the building blocks of data workflows of the visual programming environment. We have the following widgets in orange categorized according to their functionality.

Orange-Widgets

Data

These widgets read and display data. Some common examples are:

  • File: It reads the input data file and sends the dataset to its output channel.
  • CSV File Import: It reads comma-separated files and sends the dataset to its output channel.
  • Datasets: It retrieves selected datasets from the server and sends them to the output.
  • Data Table: It receives dataset(s) in its input and presents them as a spreadsheet.

Orange-Data-Widget

Visualize

These widgets visualize the given data through various graphs and bars. Some common examples are :

  • Box Plot: It shows the distributions of attribute values.
  • Distributions: It displays the value distribution of discrete or continuous attributes.
  • Scatter Plot: It provides a 2-dimensional scatter plot visualization.

Orange-Visualize-Widget

Model

These widgets apply machine learning algorithms to the given dataset(s). Some common examples are:

  • Constant: It predicts the most frequent class or means value from the training set.
  • CN2 Rule: It induces rules from data using the CN2 algorithm.
  • kNN: It predicts according to the nearest training instances.
  • Random Forest: It predicts using an ensemble of decision trees.

Orange-Model-Widget

Evaluate

These widgets evaluate the result produced by the model widget. Some common examples are :

  • Test and Score: It tests machine learning algorithms on data.
  • Predictions: It shows models’ predictions on the data.
  • Confusion Matrix: It shows proportions between the predicted and actual class.

Orange-Evaluate-Widget

Unsupervised

These widgets process unsupervised data. Some common examples are :

  • Distance Matrix: It visualizes distance measures in a distance matrix.
  • Distance Map:  It visualizes distances between objects.
  • k-Means: It applies the k-Means algorithm to the data.

Orange-Unsupervised-Widget

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