Feature Scaling with Alteryx & Python

Feature Scaling in Alteryx with Python

Alteryx Analytics is the leading platform for self-service data analytics. It provides analysts with the unique ability to easily prep, blend, and analyze all their data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks.

Python is a general-purpose programming language that can be used to develop a wide variety of applications. It is one of the fastest growing programming languages in the world and has become an essential tool for many software developers and data scientists.

Python Tool Within Alteryx

The Python tool within Alteryx Designer allows data scientists to seamlessly integrate their code into workflows. Having a Jupyter notebook interface, this tool will make those familiar with Python feel right at home. The addition of Python to Alteryx has opened the door for increased collaboration between data science teams and analysts, making it easy to incorporate custom scripts into a vast array of business analytics processes and applications.

Feature Scaling

Feature scaling is an important data preprocessing step for many machine learning algorithms. Standardization is a specific method of feature scaling that rescales data to have a mean of 0 and a standard deviation of 1. In this video, JT will demonstrate step by step how to apply feature scaling using the standardization method with the Python tool in Alteryx.

The Benefits of Using Python and Alteryx for Feature Scaling

Too many tools cluttering your workflow? Don’t sweat it. The Python tool will allow you to standardize your data without the need for multiple tools. Don’t know how to calculate the standard deviation? Not a problem. Python has built-in packages that will do all the heavy lifting for you. On top of that, if you are standardizing your data, you’re probably about to put it through some sort of machine learning algorithm. If that’s the case, then you can implement your model in the same Python tool you used to scale the data. Eliminate excess steps and consolidate your entire process into one tool.


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