Predictive analytics (PA) has emerged as a go-to approach to creating data-driven business decisions. The science of PA is not new nor are the algorithms commonly used in PA. What is new is how organizations are leveraging predictive techniques and insights to drive business value.
This tutorial will provide a practitioner’s overview to building supervised and unsupervised models, focusing on the most popular algorithms: linear and logistic regression, decision trees, neural networks, and K-Means clustering. Additionally, techniques for understanding model accuracy and interpreting models will be examined. Throughout the tutorial, concepts will be illustrated with data and real use cases.
You Will Learn
- How the most popular supervised learning models work, and tips for changing influential parameters
- How to measure supervised learning model accuracy
- How to interpret regression, decision tree, and neural network models
- How the most popular unsupervised learning models work, and tips for changing influential parameters
- How to measure unsupervised learning model accuracy
- How to interpret K-means clustering models
- Business analysts, data analysts, and data scientists who need or want to learn differences between predictive modeling algorithms and how to build predictive models