Data Science

This blog is continuation of learning the Orange tool, whereby with this blog would be discussing additional about exploitation new options in tool for splitting the dataset into training and testing dataset and more features for getting the accuracy for various models and examination them. Creating the Workflow First we tend to use File widget within the canvas and load the inbuilt iris dataset in the workflow. Next Data Sampler, widget implements several data sampling methods. It outputs a sampled and a complementary dataset (with instances from the input set that are not included in the sampled dataset). The output is processed after the input dataset is provided and Sample Data is pressed.   Here I sampled the data 75% output sampled data and 25% will be complementary data set. Send the data from the Data Sampler widget to the Test and Score widget. The widget puts learning algorithms to the test. There are various sampling strategies available, including the use of distin...

Orange Tool Orange is a free and open-source toolkit for data visualization, machine learning, and data mining. It comes with a visual programming front end for exploratory qualitative data analysis and interactive data visualization. Widgets are orange components that span from simple data presentation, subset selection, and preprocessing to empirical evaluation and predictive modelling of learning algorithms. Advanced users can use Orange as a Python library for data manipulation, while visual programming is implemented through an interface in which workflows are formed by linking predefined or user-designed widgets. How to use workflows in Orange Components that read, process, and visualize data make up Orange Workflows. Widgets communicate by sending data along with a channel for communication. One widget's output is utilized as the input for another. This results in a workflow. Let's start by putting together a basic workflow for any dataset. You can use one of the inbuilt...

What is Data preprocessing? Data preprocessing describes any type of processing performed on raw data to prepare it for another processing procedure. It was traditionally used as a preliminary step for a data mining process. More recently, these techniques have evolved for training machine learning and AI models and for running inferences against them. Also, these techniques can be used in combination with a variety of data sources, including data stored in files or databases, or being emitted by streaming data systems. Data preprocessing transforms the data into a format that will be more easily and effectively processed for the purpose of the user What is scikit learn? Scikit is the most useful and robust library for machine learning in python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionally reduction via a consistence interface in python. This library which is largely written ...