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

Merging/joining datasets


Merging or joining is a mission critical step for predictive modelling and, more often than not, while working on actual problems, an analyst will be required to do it. The readers who are familiar with relational databases know how there are multiple tables connected by a common key column across which the required columns are scattered. There can be instances where two tables are joined by more than one key column. The merges and joins in Python are very similar to a table merge/join in a relational database except that it doesn't happen in a database but rather on the local computer and that these are not tables, rather data frames in pandas. For people familiar with Excel, you can find similarity with the VLOOKUP function in the sense that both are used to get an extra column of information from a sheet/table joined by a key column.

There are various ways in which two tables/data frames can be merged/joined. The most commonly used ones are Inner Join, Left Join...