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Mastering Predictive Analytics with Python
by Joseph Babcock
Publisher: Packt Publishing
Release Date: August 2016
ISBN: 9781785882715
Topic: Python
Book Description
Exploit the power of data in your business by building advanced predictive modeling
applications with Python
About This Book
Master open source Python tools to build sophisticated predictive models
Learn to identify the right machine learning algorithm for your problem with this
forward-thinking guide
Grasp the major methods of predictive modeling and move beyond the basics to a deeper
level of understanding
Who This Book Is For
This book is designed for business analysts, BI analysts, data scientists, or junior level
data analysts who are ready to move from a conceptual understanding of advanced analytics
to an expert in designing and building advanced analytics solutions using Python. You’re
expected to have basic development experience with Python.
What You Will Learn
Gain an insight into components and design decisions for an analytical application
Master the use Python notebooks for exploratory data analysis and rapid prototyping
Get to grips with applying regression, classification, clustering, and deep learning
algorithms
Discover the advanced methods to analyze structured and unstructured data
Find out how to deploy a machine learning model in a production environment
Visualize the performance of models and the insights they produce
Scale your solutions as your data grows using Python
Ensure the robustness of your analytic applications by mastering the best practices of
predictive analysis
In Detail
The volume, diversity, and speed of data available has never been greater. Powerful machine
learning methods can unlock the value in this information by finding complex relationships
and unanticipated trends. Using the Python programming language, analysts can use these
sophisticated methods to build scalable analytic applications to deliver insights that are
of tremendous value to their organizations.
In Mastering Predictive Analytics with Python, you will learn the process of turning raw
data into powerful insights. Through case studies and code examples using popular
open-source Python libraries, this book illustrates the complete development process for
analytic applications and how to quickly apply these methods to your own data to create
robust and scalable prediction services.
Covering a wide range of algorithms for classification, regression, clustering, as well
as cutting-edge techniques such as deep learning, this book illustrates not only how these
methods work, but how to implement them in practice. You will learn to choose the right
approach for your problem and how to develop engaging visualizations to bring the insights
of predictive modeling to life
Style and approach
This book emphasizes on explaining methods through example data and code, showing you
templates that you can quickly adapt to your own use cases. It focuses on both a practical
application of sophisticated algorithms and the intuitive understanding necessary to apply
the correct method to the problem at hand. Through visual examples, it also demonstrates
how to convey insights through insightful charts and reporting.
Downloading the example code for this book. You can download the example code files for
all Packt books you have purchased from your account at http://www.PacktPub.com. If you
purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register
to have the code file.
Table of Contents
Mastering Predictive Analytics with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
eBooks, discount offers, and more
Why subscribe?
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. From Data to Decisions – Getting Started with Analytic Applications
Designing an advanced analytic solution
Data layer: warehouses, lakes, and streams
Modeling layer
Deployment layer
Reporting layer
Case study: sentiment analysis of social media feeds
Data input and transformation
Sanity checking
Model development
Scoring
Visualization and reporting
Case study: targeted e-mail campaigns
Data input and transformation
Sanity checking
Model development
Scoring
Visualization and reporting
Summary
2. Exploratory Data Analysis and Visualization in Python
Exploring categorical and numerical data in IPython
Installing IPython notebook
The notebook interface
Loading and inspecting data
Basic manipulations – grouping, filtering, mapping, and pivoting
Charting with Matplotlib
Time series analysis
Cleaning and converting
Time series diagnostics
Joining signals and correlation
Working with geospatial data
Loading geospatial data
Working in the cloud
Introduction to PySpark
Creating the SparkContext
Creating an RDD
Creating a Spark DataFrame
Summary
3. Finding Patterns in the Noise – Clustering and Unsupervised Learning
Similarity and distance metrics
Numerical distance metrics
Correlation similarity metrics and time series
Similarity metrics for categorical data
K-means clustering
Affinity propagation – automatically choosing cluster numbers
k-medoids
Agglomerative clustering
Where agglomerative clustering fails
Streaming clustering in Spark
Summary
4. Connecting the Dots with Models – Regression Methods
Linear regression
Data preparation
Model fitting and evaluation
Statistical significance of regression outputs
Generalize estimating equations
Mixed effects models
Time series data
Generalized linear models
Applying regularization to linear models
Tree methods
Decision trees
Random forest
Scaling out with PySpark – predicting year of song release
Summary
5. Putting Data in its Place – Classification Methods and Analysis
Logistic regression
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