Data Science

So many decisions go into the data science process. New automated tools let machines and humans collaborate on making the right choices.

Simple enough for statistical beginners, powerful enough to help solve big problems: That’s what some data analytics students learned about linear regression in a recent data challenge. 

Today, we are very excited to announce the public preview of two Alteryx Designer tools that connect to Azure Machine Learning automated ML (Machine Learning), allowing you to easily leverage the power of automated machine learning from inside Designer.

With the Azure Machine Learning Training tool, you can send data directly from Designer to be trained by Azure Machine Learning automated ML. Use the Azure Machine Learning Scoring tool to use the model you trained for predictions on new data. 

Neural Networks are an approach to artificial intelligence that was first proposed in 1944.  Modeled loosely on the human brain, Neural Networks consist of a multitude of simple processing nodes (called neurons) that are highly interconnected and send data through these network connections to estimate a target variable. In this article, I will discuss the structure and training of simple neural networks (specifically Multilayer Perceptrons, aka "vanilla neural networks"), as well as demonstrate an example neural network created by the Alteryx Neural Network Tool.

Building my first linear regression model turned me into an instant celebrity. My roommate, who has acted as a sounding board for my predictive-analytics-learning progress, now believes I can use Linear Regression to predict the winner of the next horse race. While it would be fun to try, a more applicable use case is predicting how much a customer will spend (which, in the case of horse racing could translate to how much someone might spend on a bet). For my use case, I want to predict how much a Lyft driver can expect to receive on their next fare.

New to Alteryx and ready to help improve the documentation, I'm clambering up another predictive suite tool: the decision tree.

What is support vector machine and why should you use it?

An introduction to working with random forests using Python

A high level introduction to what linear regression is and how it works.

As a fairly new developer on the Core Engines team, I am thrilled to have the opportunity of adding functionality to the R Tool.