This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
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.