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

Applying the Random Forest model to a regression analysis


Random Forest, similar to decision trees, can also be applied to solving regression problems. We used them previously to classify calls (refer to the Predicting subscribers with random tree forests recipe in Chapter 3, Classification Techniques). Here, we will use Random Forest to predict the output of a power plant.

Getting ready

To execute this, you will need pandas, NumPy, and Scikit. No other prerequisites are required.

How to do it…

The Random Forests are part of the ensemble types of models. This example borrows the code-shell that we presented in Chapter 3, Classification Techniques (the regression_randomForest.py file):

import sys
sys.path.append('..')

# the rest of the imports
import helper as hlp
import pandas as pd
import numpy as np
import sklearn.ensemble as en
import sklearn.cross_validation as cv

@hlp.timeit
def regression_rf(x,y):
    '''
        Estimate a random forest regressor
    '''
    # create the regressor object...