Book Image

Practical Data Analysis Cookbook

By : Tomasz Drabas
Book Image

Practical Data Analysis Cookbook

By: Tomasz Drabas

Overview of this book

Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors. This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more. First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews. By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.
Table of Contents (19 chapters)
Practical Data Analysis Cookbook
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Estimating the well-known Multinomial Logit model


The Multinomial Logit (MNL) model was introduced by Daniel McFadden in his seminal paper from 1973, http://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf.

The model is based on fairly restrictive assumptions: the Independent and Identically Distributed (IID) error terms and the Independence from Irrelevant Alternatives (IIA).

The IID assumes that the error terms of utility functions of all the alternatives are independent (uncorrelated) and follow the same distribution. (For MNL, it is the Extreme Value Type I Distribution, commonly known as Gumbel distribution after E. J. Gumbel who derived and analyzed it.)

The IIA, on the other hand, assumes that the ratios of probabilities between alternatives are constant, that is, removing one or more alternatives from the consideration set does not change the ratio between the remaining alternatives. Consider the following situation: you are choosing between a bike, train, and car to get to work. For...