Mastering Predictive Analytics with Python

Exploit the power of data in your business by building advanced predictive modeling applications with Python
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Mastering Predictive Analytics with Python

Joseph Babcock

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Exploit the power of data in your business by building advanced predictive modeling applications with Python

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Book Details

ISBN 139781785882715
Paperback334 pages

Book Description

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

Table of Contents

Chapter 1: From Data to Decisions – Getting Started with Analytic Applications
Designing an advanced analytic solution
Case study: sentiment analysis of social media feeds
Case study: targeted e-mail campaigns
Summary
Chapter 2: Exploratory Data Analysis and Visualization in Python
Exploring categorical and numerical data in IPython
Time series analysis
Working with geospatial data
Introduction to PySpark
Summary
Chapter 3: Finding Patterns in the Noise – Clustering and Unsupervised Learning
Similarity and distance metrics
Affinity propagation – automatically choosing cluster numbers
k-medoids
Agglomerative clustering
Streaming clustering in Spark
Summary
Chapter 4: Connecting the Dots with Models – Regression Methods
Linear regression
Tree methods
Scaling out with PySpark – predicting year of song release
Summary
Chapter 5: Putting Data in its Place – Classification Methods and Analysis
Logistic regression
Fitting the model
Evaluating classification models
Separating Nonlinear boundaries with Support vector machines
Comparing classification methods
Case study: fitting classifier models in pyspark
Summary
Chapter 6: Words and Pixels – Working with Unstructured Data
Working with textual data
Principal component analysis
Images
Case Study: Training a Recommender System in PySpark
Summary
Chapter 7: Learning from the Bottom Up – Deep Networks and Unsupervised Features
Learning patterns with neural networks
The TensorFlow library and digit recognition
Summary
Chapter 8: Sharing Models with Prediction Services
The architecture of a prediction service
Clients and making requests
Server – the web traffic controller
Persisting information with database systems
Case study – logistic regression service
Summary
Chapter 9: Reporting and Testing – Iterating on Analytic Systems
Checking the health of models with diagnostics
Iterating on models through A/B testing
Guidelines for communication
Summary

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

Authors

Table of Contents

Chapter 1: From Data to Decisions – Getting Started with Analytic Applications
Designing an advanced analytic solution
Case study: sentiment analysis of social media feeds
Case study: targeted e-mail campaigns
Summary
Chapter 2: Exploratory Data Analysis and Visualization in Python
Exploring categorical and numerical data in IPython
Time series analysis
Working with geospatial data
Introduction to PySpark
Summary
Chapter 3: Finding Patterns in the Noise – Clustering and Unsupervised Learning
Similarity and distance metrics
Affinity propagation – automatically choosing cluster numbers
k-medoids
Agglomerative clustering
Streaming clustering in Spark
Summary
Chapter 4: Connecting the Dots with Models – Regression Methods
Linear regression
Tree methods
Scaling out with PySpark – predicting year of song release
Summary
Chapter 5: Putting Data in its Place – Classification Methods and Analysis
Logistic regression
Fitting the model
Evaluating classification models
Separating Nonlinear boundaries with Support vector machines
Comparing classification methods
Case study: fitting classifier models in pyspark
Summary
Chapter 6: Words and Pixels – Working with Unstructured Data
Working with textual data
Principal component analysis
Images
Case Study: Training a Recommender System in PySpark
Summary
Chapter 7: Learning from the Bottom Up – Deep Networks and Unsupervised Features
Learning patterns with neural networks
The TensorFlow library and digit recognition
Summary
Chapter 8: Sharing Models with Prediction Services
The architecture of a prediction service
Clients and making requests
Server – the web traffic controller
Persisting information with database systems
Case study – logistic regression service
Summary
Chapter 9: Reporting and Testing – Iterating on Analytic Systems
Checking the health of models with diagnostics
Iterating on models through A/B testing
Guidelines for communication
Summary

Book Details

ISBN 139781785882715
Paperback334 pages
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