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Index
Cover
Table of Contents
Mastering Predictive Analytics with Python
Mastering Predictive Analytics with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
1. From Data to Decisions – Getting Started with Analytic Applications
Case study: sentiment analysis of social media feeds
Case study: targeted e-mail campaigns
Summary
2. Exploratory Data Analysis and Visualization in Python
Time series analysis
Working with geospatial data
Introduction to PySpark
Summary
3. Finding Patterns in the Noise – Clustering and Unsupervised Learning
Affinity propagation – automatically choosing cluster numbers
k-medoids
Agglomerative clustering
Streaming clustering in Spark
Summary
4. Connecting the Dots with Models – Regression Methods
Tree methods
Scaling out with PySpark – predicting year of song release
Summary
5. Putting Data in its Place – Classification Methods and Analysis
Fitting the model
Evaluating classification models
Separating Nonlinear boundaries with Support vector machines
Comparing classification methods
Case study: fitting classifier models in pyspark
Summary
6. Words and Pixels – Working with Unstructured Data
Principal component analysis
Images
Case Study: Training a Recommender System in PySpark
Summary
7. Learning from the Bottom Up – Deep Networks and Unsupervised Features
The TensorFlow library and digit recognition
Summary
8. Sharing Models with Prediction Services
Clients and making requests
Server – the web traffic controller
Persisting information with database systems
Case study – logistic regression service
Summary
9. Reporting and Testing – Iterating on Analytic Systems
Iterating on models through A/B testing
Guidelines for communication
Summary
Index
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