Introduction of Orange Tools
This blog is all about introduction of Orange Tool . We can do a lot of stuff with the help of the orange tool like visual programming, data visualization, data exploration, data mining and many more . The orange tool is free and open-source and you can install it very easily on any operating system.
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 . Orange quickly formats the data in a pattern that you can move with widgets.
For download click here .
Let’s start with Orange Tool ,
Here you can see the canvas of orange where you will do all your data exploration. On the left-hand side, there is a total of 5 sections and that all 5 sections contain different-different widgets like data , Visualize , Model , Evaluate and Unsupervised which we will use in the future for data exploration and evaluation of different model .
Orange consists of a canvas interface onto which the user places widgets and creates a data analysis workflow. Widgets offer basic functionalities such as reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc.
For this tutorial we use California Housing Prices dataset .
Step-1
We have to import our dataset . Orange can load data from any of the major standard spreadsheet file types, like CSV and Excel .
In our case we have to give file location of our dataset .
For basic information like number of rows and column , number of numeric and categorical column and many information we use function data info .
Step-2
We have to visualize our dataset .
Step-3
Here for cleaning purpose, we will impute missing values.
Imputation is a very important step in understanding and making the best use of our data.
Here, I have selected the default method to be Average for numerical values and Most Frequent for text based values (categorical).
Step-4
Create a machine learning model using Orange Tool . For our dataset we ae using simple logistic regression model .
After applying model , we have to evaluate our model . For evaluating model we use test and score method .
step-5
And last we created confusion matrix .
Other Widgets Information here.
Github Link
https://github.com/dhruvi410/-Introduction-to-Orange-Tools
Conclusion
I hope now you can work by yourself in the orange tool. I tried to cover as many things as I can. Now you can explore more by yourself.
Do check out more features of the Orange tool here.
