Mastering Predictive Analytics with Python电子书

Mastering Predictive Analytics with Python

Table of Contents

Mastering Predictive Analytics with Python

Credits

About the Author

About the Reviewer

www.PacktPub.com

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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

Multiclass logistic classifiers: multinomial regression

Formatting a dataset for classification problems

Learning pointwise updates with stochastic gradient descent

Jointly optimizing all parameters with second-order methods

Fitting the model

Evaluating classification models

Strategies for improving classification models

Separating Nonlinear boundaries with Support vector machines

Fitting and SVM to the census data

Boosting – combining small models to improve accuracy

Gradient boosted decision trees

Comparing classification methods

Case study: fitting classifier models in pyspark

Summary

6. Words and Pixels – Working with Unstructured Data

Working with textual data

Cleaning textual data

Extracting features from textual data

Using dimensionality reduction to simplify datasets

Principal component analysis

Latent Dirichlet Allocation

Using dimensionality reduction in predictive modeling

Images

Cleaning image data

Thresholding images to highlight objects

Dimensionality reduction for image analysis

Case Study: Training a Recommender System in PySpark

Summary

7. Learning from the Bottom Up – Deep Networks and Unsupervised Features

Learning patterns with neural networks

A network of one – the perceptron

Combining perceptrons – a single-layer neural network

Parameter fitting with back-propagation

Discriminative versus generative models

Vanishing gradients and explaining away

Pretraining belief networks

Using dropout to regularize networks

Convolutional networks and rectified units

Compressing Data with autoencoder networks

Optimizing the learning rate

The TensorFlow library and digit recognition

The MNIST data

Constructing the network

Summary

8. Sharing Models with Prediction Services

The architecture of a prediction service

Clients and making requests

The GET requests

The POST request

The HEAD request

The PUT request

The DELETE request

Server – the web traffic controller

Application – the engine of the predictive services

Persisting information with database systems

Case study – logistic regression service

Setting up the database

The web server

The web application

The flow of a prediction service – training a model

On-demand and bulk prediction

Summary

9. Reporting and Testing – Iterating on Analytic Systems

Checking the health of models with diagnostics

Evaluating changes in model performance

Changes in feature importance

Changes in unsupervised model performance

Iterating on models through A/B testing

Experimental allocation – assigning customers to experiments

Deciding a sample size

Multiple hypothesis testing

Guidelines for communication

Translate terms to business values

Visualizing results

Case Study: building a reporting service

The report server

The report application

The visualization layer

Summary

Index