With the number of different techniques that can be used in predictive analytics, choosing the right one can be overwhelming. By understanding the nature of the desired prediction and recognizing the types of data available, the appropriate modeling technique will become clear. In this video, Matt North will show you some of the key analysis points that will help you select the correct technique to use for your data.

Using RapidMiner, and based on the the outcome you are looking for, you will examine four popular techniques used in predictive analytics and explore when it might be appropriate to use each one. These techniques are important to business analysts and data scientists that are using statistical prediction in a business setting. Matt also discusses statistics and the roles of independant and dependant variables; and a clear understanding of basic regression, a neural network, logistic regression, and a decision tree model will be helpful for getting the most from this video.

• learn how to apply linear regression, neural networks, logistic regression, and decision tree models to your data in RapidMiner
• understand when and why to apply each of the four models examined
• you will learn to assess if your data is appropriate for certain types of predictive modeling

Matt North is a professor of Information Systems at Utah Valley University, where he teaches courses on data analytics and database development, administration and security. He holds degrees from BYU, Utah State University and West Virginia University. He served as a Fulbright appointee at Universidad Tecnológica Nacional in Argentina, and is the recipient of the International Association for Computer Information Systems’ Ben Bauman Award for Excellence, and the Gamma Sigma Alpha Outstanding Professor Award. He is the author of numerous articles, published papers and book chapters, in addition to his two books: "Data Mining for the Masses", and "Life Lessons and Leadership".

Other videos in this series:

Does Correlation Prove Causation in Predictive Analytics?
How Can I Clean My Data for Use in a Predictive Model?