Mastering predictive analytics with Python : exploit the power of data in your business by building advanced predictive modeling applications with Python / Joseph Babcock.

Format:

Book

Author/Creator:

Babcock, Joseph, author.

Series:

Community experience distilled.

Community Experience Distilled

Language:

English

Subjects (All):

Business planning--Computer programs.

Business planning.

Physical Description:

1 online resource (335 pages) : illustrations.

Edition:

1st edition

Place of Publication:

Birmingham, England : Packt Publishing, 2016.

System Details:

text file

Summary:

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 ...

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewer

www.PacktPub.com

Table of Contents

Preface

Chapter 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

Summary

Chapter 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

Chapter 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

Chapter 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

Chapter 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

Chapter 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

Chapter 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

Chapter 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

Chapter 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

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed March 1, 2017).

ISBN:

9781785889820

1785889826

OCLC:

958874809