4 min readSep 2, 2021

Practical:-4

AIM:- Introduction of Orange Tools

This blog is all about an orange tool for data mining. We can do a lot of stuff with the help of the orange tool like visual programming, data visualization, data exploration, data mining, etc… The orange tool is free and open-source and you can install it very easily on any os.

What is orange?

Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative data analysis and interactive data visualization, and can also be used as a Python library.

Classifications: data visualization, machine lea…

Programming languages used: Python

Developer: Open-source software

Download source :-

For windows download click here.

Let started the orange tools,

Here you can see the canvas of orange where you will do all your data exploration. On the left-hand side, you can see there is a total of 5 sections and that all 5 sections contain different-different widgets which we will use in the future for data exploration.

Workflow:-

Workflows in Orange resemble actual optical systems; setting up a simulation in this way is intuitive and easy to inspect and modify. Passing data from one widget to another imposes no additional overhead in terms of CPU time.

Above image, we can see that how the workflow will use and connect with another with the workflow.

Now we learn about how to step-by-step process of creating the workflow.

Step 1:-

In the image you can see by default data set will be available in the file sections like iris.tab,heart_disease.tab, etc. Also, we can upload our data set from the local pc as well as fetch the data from the URL.

Now I load the data using a URL.

URL:- https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv

Step 2:-

Now after the load the data we have created Scatter Plot for our data. Below image you can see the Scatter plot for iris data set.

Step 3:-

After that I have created bar chart using Distribution. Below image you can see that things.

Step 4:-

Here I have created work flow with the data table. In Image you can see the data table for iris data set.

Step 5:-

Now we created an ML model using orange. For Iris's data set I have used Logistic Regression. In the image, you can see the accuracy, classification score, F1, Precision, and recall for the iris data set.