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

Predicting subscribers with random tree forests


Random forests belong to a family of ensemble models. The ensemble models work on a premise that two brains are better than one; they combine the predictions of many weaker models (decision trees) to come up with a prediction that reflects a mode among these weaker models. For more, check https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm.

Getting ready

To execute this recipe, you will need pandas and scikit-learn. No other prerequisites are required.

How to do it…

As in previous examples, Scikit provides an easy way of building a random forest classifier (the classification_randomForest.py file):

import sklearn.ensemble as en

@hlp.timeit
def fitRandomForest(data):
    '''
        Build a random forest classifier
    '''
    # create the classifier object
    forest = en.RandomForestClassifier(n_jobs=-1,
        min_samples_split=100, n_estimators=10,
        class_weight="auto")

    # fit the data
    return forest.fit(data[0],data...