Applied Predictive Modeling: An Overview

Summary

Predictive modeling uses statistics in order to predict outcomes. However, predictive modeling can be applied to events regardless of time of occurrence. When it comes to the applications of predictive modeling, techniques are used in various fields including algorithmic trading, uplift modeling, archaeology, health care, customer relationship management, and many others. This book covers the predictive modeling process with fundamental steps of the process, data preprocessing, data splitting and crucial steps of model tuning and improving model performance. Further, the book will introduce you to the most common classification and regression techniques including logistic regression which is widely used when it comes to the finding the probability of event success or event failure. You will get to know the common predictive modeling techniques as well such as stepwise regression, polynomial regression, and ridge regression which will help you when you are dealing with the data that suffers from very common multicollinearity where independent variables are highly correlated. What you will learn in Applied Predictive Modeling: Most common predictive modeling techniques Types of regression models The overall predictive modeling process Fundamental steps to effective and highly accurate predictive modeling How to build predictive model with logistic regression with code listings How to build predictive model using Python How to enhance your model performance Parameters for increasing the overall predictive power How to handle class imbalance Common causes of poor model performance Listen to this book now and learn more about applied predictive modeling!

©2017 Steven Taylor (P)2017 Steven Taylor