Book Image

Learning Predictive Analytics with Python

By : Ashish Kumar, Gary Dougan
Book Image

Learning Predictive Analytics with Python

By: Ashish Kumar, Gary Dougan

Overview of this book

Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.
Table of Contents (19 chapters)
Learning Predictive Analytics with Python
Credits
Foreword
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
A List of Links
Index

Case 3 – reading data from a URL


Several times, we need to read the data directly from a web URL. This URL might contain the data written in it or might contain a file which has the data. For example, navigate to this website, http://winterolympicsmedals.com/ which lists the medals won by various countries in different sports during the Winter Olympics. Now type the following address in the URL address bar: http://winterolympicsmedals.com/medals.csv.

A CSV file will be downloaded automatically. If you choose to download it manually, saving it and then specifying the directory path for the read_csv method is a time consuming process. Instead, Python allows us to read such files directly from the URL. Apart from the significant saving in time, it is also beneficial to loop over the files when there are many such files to be downloaded and read in.

A simple read_csv statement is required to read the data directly from the URL:

import pandas as pd
medal_data=pd.read_csv('http://winterolympicsmedals...