Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.This books develops the more important predictive models like Regression Models, Generalized Regression Models, Discrete Choice Models, Logit and Probit Models, Support Vector Machine Regression, Gaussian Process Regresion, Regression Trees, Regression Models with Neural Networks and Neural Networks Time Series Prediction.