Regular price: $6.95

Free with 30-day trial
Membership details Membership details
  • A 30-day trial plus your first audiobook, free
  • 1 credit/month after trial – good for any book, any price
  • Easy exchanges – swap any book you don’t love
  • Keep your audiobooks, even if you cancel
  • After your trial, Audible is just $14.95/month
OR
In Cart

Publisher's 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

What members say

Average Customer Ratings

Overall

  • 4.9 out of 5.0
  • 5 Stars
    14
  • 4 Stars
    1
  • 3 Stars
    0
  • 2 Stars
    0
  • 1 Stars
    0

Performance

  • 4.9 out of 5.0
  • 5 Stars
    14
  • 4 Stars
    1
  • 3 Stars
    0
  • 2 Stars
    0
  • 1 Stars
    0

Story

  • 4.9 out of 5.0
  • 5 Stars
    14
  • 4 Stars
    1
  • 3 Stars
    0
  • 2 Stars
    0
  • 1 Stars
    0
Sort by:
  • Overall
  • Performance
  • Story

5/5 great Grab..

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..
Highly suggest It...

  • Overall
  • Performance
  • Story

I found it so informative..

The author describes the science behind the models and includes some problems involved in using these models as well as some methods for overcoming them.

  • Overall
  • Performance
  • Story

Nice narration! I like it! I bought this audiobook

The book is clear, concise, and relatively comprehensive. Worth my time and money. The examples are very interesting with clear discussions. The author and translator have done great job in making complicated topic approachable.

  • Overall
  • Performance
  • Story

Just what I was looking for!

It is a very good book easy to listen and follow but I found that is necessary to all who in need of this topic..

  • Overall
  • Performance
  • Story

Comprehensive!!

Excellent insights on the strengths and weaknesses of traditional Applied Predictive Modeling..
HIGHLY RECOMMENDED BOOK..
HELPFUL BOOK...

  • Overall
  • Performance
  • Story

The topic was great ...

This book has been a great Listen. Covers all the fundamentals and walks you through the problems step by step. Highly recommended!

  • Overall
  • Performance
  • Story

a great story, a great performer, a great time..

A good practical introduction to predictive modeling and machine learning. It covers most important topics in predictive modeling. One reason why I recommend this book is that it contains several case studies demonstrating how to apply machine learning to solve real-world problems

  • Overall
  • Performance
  • Story

Author Nailed it...

Excellent book. For the practitioner it is a valuable reference book to pull off the shelf for the myriad of models presents. For the novice, it will allow one to get quickly acclimated to applied modeling with a case based approach. Some caveats: R specific and the theoretical aspects are not too deeply discussed. Both caveats are addressed upfront by the Author.

  • Overall
  • Performance
  • Story

Excellent book!

There are a number of examples utilizing quantitative and qualitative attributes with tips on how best to preprocess each followed by an explanation of how to apply certain models to particular problems within R. Both qualitative and quantitative targets get substantial treatment in the book, as does the process of measuring and comparing model performance in each instance.

  • Overall
  • Performance
  • Story

Recommended!

Applied Predictive Modeling is one of the few good examples where the word applied is aptly used for a book title. Like other texts on predictive modeling, the material in this book covers the why, but more importantly the authors detail the how-to in the predictive modeling process.