Book description
Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python
About This Book
 A stepbystep guide to predictive modeling including lots of tips, tricks, and best practices
 Get to grips with the basics of Predictive Analytics with Python
 Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering
Who This Book Is For
If you wish to learn how to implement Predictive Analytics algorithms using Python libraries, then this is the book for you. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about Predictive Analytics algorithms, this book will also help you. The book will be beneficial to and can be read by any Data Science enthusiasts. Some familiarity with Python will be useful to get the most out of this book, but it is certainly not a prerequisite.
What You Will Learn
 Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries
 Analyze the result parameters arising from the implementation of Predictive Analytics algorithms
 Write Python modules/functions from scratch to execute segments or the whole of these algorithms
 Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms
 Get to know various methods of importing, cleaning, subsetting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy
 Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries
 Understand the best practices while handling datasets in Python and creating predictive models out of them
In Detail
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, scikitlearn, 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.
Style and approach
All the concepts in this book been explained and illustrated using a dataset, and in a stepbystep manner. The Python code snippet to implement a method or concept is followed by the output, such as charts, dataset heads, pictures, and so on. The statistical concepts are explained in detail wherever required.
Table of contents

Learning Predictive Analytics with Python
 Table of Contents
 Learning Predictive Analytics with Python
 Credits
 Foreword
 About the Author
 Acknowledgments
 About the Reviewer
 www.PacktPub.com
 Preface
 1. Getting Started with Predictive Modelling

2. Data Cleaning
 Reading the data – variations and examples
 Various methods of importing data in Python
 The read_csv method
 Use cases of the read_csv method
 Case 2 – reading a dataset using the open method of Python
 Case 3 – reading data from a URL
 Case 4 – miscellaneous cases
 Basics – summary, dimensions, and structure
 Handling missing values
 Creating dummy variables
 Visualizing a dataset by basic plotting
 Summary

3. Data Wrangling
 Subsetting a dataset
 Generating random numbers and their usage
 Grouping the data – aggregation, filtering, and transformation
 Random sampling – splitting a dataset in training and testing datasets
 Concatenating and appending data
 Merging/joining datasets
 Summary
 4. Statistical Concepts for Predictive Modelling
 5. Linear Regression with Python

6. Logistic Regression with Python
 Linear regression versus logistic regression
 Understanding the math behind logistic regression
 Implementing logistic regression with Python
 Model validation and evaluation
 Model validation
 Summary
 7. Clustering with Python
 8. Trees and Random Forests with Python
 9. Best Practices for Predictive Modelling
 A. A List of Links
 Index
Product information
 Title: Learning Predictive Analytics with Python
 Author(s):
 Release date: February 2016
 Publisher(s): Packt Publishing
 ISBN: 9781783983261
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