Learning Predictive Analytics with Python – Ashish Kumar


Ashish Kumar
Learning Predictive Analytics with Python

Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python.

About This Book
A step-by-step 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, sub-setting, 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, 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.

Style and approach
All the concepts in this book been explained and illustrated using a dataset, and in a step-by-step 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.

Dean Abbott
Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst

Learn the art and science of predictive analytics — techniques that get results

Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included.

The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today
This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions
Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish
Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios
A companion website provides all the data sets used to generate the examples as well as a free trial version of software

Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

Anasse Bari, Mohamed Chaouchi, Tommy Jung
Predictive Analytics For Dummies

Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions. Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more. Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech–savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts–and–bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies .

Ferran Garcia Pagans
Predictive Analytics using Rattle and Qlik Sense

If you are a business analyst who wants to understand how to improve your data analysis and how to apply predictive analytics, then this book is ideal for you. This book assumes you have some basic knowledge of statistics and a spreadsheet editor such as Excel, but knowledge of QlikView is not required.

Create comprehensive solutions for predictive analysis using Rattle and share them with Qlik Sense

About This Book

Create visualizations, dashboards, and data applications with Qlik Sense and Rattle
Load, explore, and manipulate data to Rattle to create predictions and discover hidden patterns in the data
A step-by-step guide to learning predictive analytics in a quick and easy way

What You Will Learn

Set up your desktop environment by installing Qlik Sense Desktop, R, and Rattle
Explore Rattle charts and the most commonly used multivariate statistical techniques to discover relationships among data
Find solutions to business questions by applying data analysis techniques
Use unsupervised and supervised learning methods to gain insights into your data
Evaluate the performance of a predictive model
Create basic charts and filters using Qlik Sense Desktop to build your first data application
Improve your analysis by complementing Qlik Sense Desktop with predictive analytics
Familiarize yourself with the basics of data visualization and data storytelling
In Detail

Qlik Sense Desktop, the personal and free version of Qlik Sense, is a powerful tool for business analysts to analyze data and create useful data applications. Rattle, developed in R, is a GUI used for data mining and complements Qlik Sense Desktop very well. By combining Rattle and Qlik Sense Desktop, a business user can learn how to apply predictive analytics to create real-world data applications. The objective is to use Qlik Sense to analyze data and complement it with predictive analytics using Rattle.

This book will introduce you to basic predictive analysis techniques using Rattle and basic data visualizations concepts using Qlik Sense Desktop. You will start by setting up Qlik Sense Desktop, R, and Rattle and learn the basic of these tools. Then this book will examine the data and make it ready to be analyzed. After that, you will get to know the key concepts of predictive analytics, by building simple models with Rattle and creating visualizations with Qlik Sense Desktop. Finally, the book will show you the basics of data visualization and will help you to create your first data application and dashboard.

Eric Siegel, Thomas H. Davenport
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

“The Freakonomics of big data.”
—Stein Kretsinger, founding executive of Advertising.com; former lead analyst at Capital One
This book is easily understood by all readers. Rather than a “how to” for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.

You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, “I knew you were going to do that!” These institutions are seizing upon the power to predict whether you’re going to click, buy, lie, or die.

Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.

How? Prediction is powered by the world’s most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.

Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.

In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:

• What type of mortgage risk Chase Bank predicted before the recession.
• Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves.
• Why early retirement decreases life expectancy and vegetarians miss fewer flights.
• Five reasons why organizations predict death, including one health insurance company.
• How U.S. Bank, European wireless carrier Telenor, and Obama’s 2012 campaign calculated the way to most strongly influence each individual.
• How IBM’s Watson computer used predictive modeling to answer questions and beat the human champs on TV’s Jeopardy!
• How companies ascertain untold, private truths — how Target figures out you’re pregnant and Hewlett-Packard deduces you’re about to quit your job.
• How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free.
• What’s predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia.
A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.

Predictive analytics transcends human perception. This book’s final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can’t even be sure has happened afterward — but that can be predicted in advance?

Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

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