Ebooks

Applied Predictive Modeling

Predictive modeling uses statistics in order to predict outcomes. However, predictive modeling can be applied to future and to any other kind of unknown event, regardless of when it happened. 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.

The text then provides fundamental steps to effective predictive modeling. In the second chapter, you will learn how to build your own predictive model with logistic regression and Python. You will find data sets as well as corresponding codes. On of the crucial predictive modeling steps is model tuning, so you will learn some common techniques used in order to improve your model performance. You will get to know how to tune the parameters commonly used to increase the overall predictive power. Predictive modeling comes with a few obstacles and challenges like class imbalance. Imbalanced classes commonly put the accuracy of the model out of business, but you will learn how to properly handle class imbalance which will significantly improve the accuracy of your model. The book is multi-purpose focused on to predictive modeling process and predictive modeling techniques, so it will be of great help for those who are interested in predictive modeling techniques and applications. So, it is the right time to simplify the analysis, boost productivity as well as save time. The book will be your companion on your journey towards highly accurate predictive models.

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

Get this book now and learn more about Applied Predictive Modeling!

A Book Bundle of Probability with Permutations and Markov Models

Get two books in one now!!

Probability with Permutations: An Introduction to Probability and Combinations

Understanding probability as unique and stimulating theory which goes beyond conventional mathematics, will give you better perspective of the world around you. The first part of the book explains the fundamentals of probability in clear and easy to understand way even if you are not familiar with mathematics at all and you are just starting your journey towards this particular field of science. In the following sections of the book, the subject is explained in wider context along with importance of permutations and combinations in probability and their applications to a variety of scientific problems as well as the importance of probability in real life situations.

Markov Models: An Introduction to Markov Models

This book will offer you an insight into the Hidden Markov Models as well as the Bayesian Networks. Additionally, by reading this book, you will also learn algorithms such as Markov Chain Sampling. Furthermore, this book will also teach you how Markov Models are very relevant when a decision problem is associated with a risk that continues over time, when the timing of occurrences is vital as well as when events occur more than once. This book highlights several applications of Markov Models. Lastly, after purchasing this book, you will need to put in a lot of effort and time for you to reap the maximum benefits.

By Downloading This Book Bundle Now You Will Discover:

History of Probability

Explanation of Combinations

Probability Using Permutations and Combinations

Urn Problems

Probability and Lottery

Probability and Gambling

Applications of Probability

Hidden Markov Models

Dynamic Bayesian Networks

Stepwise Mutations using the Wright Fisher Model

Using Normalized Algorithms to Update the Formulas

Types of Markov Processes

Important Tools used with HMM

Machine Learning

And much much more!

Download this book bundle now and learn more about Probability with Permutations and Markov Models!

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