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Mastering Predictive Analytics with Python
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Mastering Predictive Analytics,with Python, Exploit the power of data in your business by building. advanced predictive modeling applications with Python. Joseph Babcock,BIRMINGHAM MUMBAI,Mastering Predictive Analytics with Python. Copyright 2016 Packt Publishing, All rights reserved No part of this book may be reproduced stored in a retrieval. system or transmitted in any form or by any means without the prior written. permission of the publisher except in the case of brief quotations embedded in. critical articles or reviews, Every effort has been made in the preparation of this book to ensure the accuracy. of the information presented However the information contained in this book is. sold without warranty either express or implied Neither the author nor Packt. Publishing and its dealers and distributors will be held liable for any damages. caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the. companies and products mentioned in this book by the appropriate use of capitals. However Packt Publishing cannot guarantee the accuracy of this information. First published August 2016,Production reference 1290816.
Published by Packt Publishing Ltd,Livery Place,35 Livery Street. Birmingham B3 2PB UK,ISBN 978 1 78588 271 5,www packtpub com. Author Project Coordinator,Joseph Babcock Shweta H Birwatkar. Reviewer Proofreader,Dipanjan Deb Safis Editing,Commissioning Editor Indexer. Kartikey Pandey Monica Ajmera Mehta,Acquisition Editor Graphics.
Aaron Lazar Kirk D Pinha,Content Development Editor Production Coordinator. Sumeet Sawant Nilesh Mohite,Technical Editor Cover Work. Utkarsha S Kadam Nilesh Mohite,Copy Editor,Vikrant Phadke. About the Author, Joseph Babcock has spent almost a decade exploring complex datasets and. combining predictive modeling with visualization to understand correlations and. forecast anticipated outcomes He received a PhD from the Solomon H Snyder. Department of Neuroscience at The Johns Hopkins University School of Medicine. where he used machine learning to predict adverse cardiac side effects of drugs. Outside the academy he has tackled big data challenges in the healthcare and. entertainment industries,About the Reviewer, Dipanjan Deb is an experienced analytics professional with 16 years of cumulative.
experience in machine statistical learning data mining and predictive analytics. across the healthcare maritime automotive energy CPG and human resource. domains He is highly proficient in developing cutting edge analytic solutions using. open source and commercial packages to integrate multiple systems in order to. provide massively parallelized and large scale optimization. Dipanjan has extensive experience in building analytics teams of data scientists that. deliver high quality solutions He strategizes and collaborates with industry experts. technical experts and data scientists to build analytic solutions that shorten the. transition from a POC to a commercial release, He is well versed in overarching supervised semi supervised and unsupervised. learning algorithm implementations in R Python Vowpal Wabbit Julia and SAS. Distributed frameworks including Hadoop and Spark both in premise and in cloud. environment He is a part time Kaggler and IOT IIOT enthusiast Raspberry Pi and. Arduino prototyping,www PacktPub com,eBooks discount offers and more. Did you know that Packt offers eBook versions of every book published with PDF. and ePub files available You can upgrade to the eBook version at www PacktPub. com and as a print book customer you are entitled to a discount on the eBook copy. Get in touch with us at customercare packtpub com for more details. At www PacktPub com you can also read a collection of free technical articles sign. up for a range of free newsletters and receive exclusive discounts and offers on Packt. books and eBooks, https www2 packtpub com books subscription packtlib. Do you need instant solutions to your IT questions PacktLib is Packt s online digital. book library Here you can search access and read Packt s entire library of books. Why subscribe, Fully searchable across every book published by Packt. Copy and paste print and bookmark content,On demand and accessible via a web browser.
Table of Contents,Preface vii, Chapter 1 From Data to Decisions Getting Started with. Analytic Applications 1,Designing an advanced analytic solution 4. Data layer warehouses lakes and streams 6,Modeling layer 8. Deployment layer 14,Reporting layer 15, Case study sentiment analysis of social media feeds 16. Data input and transformation 17,Sanity checking 18.
Model development 18,Scoring 19,Visualization and reporting 19. Case study targeted e mail campaigns 19,Data input and transformation 20. Sanity checking 21,Model development 21,Scoring 21. Visualization and reporting 21,Summary 23, Chapter 2 Exploratory Data Analysis and Visualization in Python 25. Exploring categorical and numerical data in IPython 26. Installing IPython notebook 27,The notebook interface 27.
Loading and inspecting data 30, Basic manipulations grouping filtering mapping and pivoting 33. Charting with Matplotlib 38,Table of Contents,Time series analysis 46. Cleaning and converting 46,Time series diagnostics 48. Joining signals and correlation 50,Working with geospatial data 53. Loading geospatial data 53,Working in the cloud 55.
Introduction to PySpark 56,Creating the SparkContext 56. Creating an RDD 58,Creating a Spark DataFrame 59,Summary 61. Chapter 3 Finding Patterns in the Noise Clustering and. Unsupervised Learning 63,Similarity and distance metrics 64. Numerical distance metrics 64,Correlation similarity metrics and time series 70. Similarity metrics for categorical data 78,K means clustering 83.
Affinity propagation automatically choosing cluster numbers 89. k medoids 93,Agglomerative clustering 94,Where agglomerative clustering fails 96. Streaming clustering in Spark 100,Summary 104, Chapter 4 Connecting the Dots with Models Regression. Methods 105,Linear regression 106,Data preparation 109. Model fitting and evaluation 114, Statistical significance of regression outputs 119. Generalize estimating equations 124,Mixed effects models 126.
Time series data 127,Generalized linear models 128. Applying regularization to linear models 129,Tree methods 132. Decision trees 132,Random forest 138,Table of Contents. Scaling out with PySpark predicting year of song release 141. Summary 143, Chapter 5 Putting Data in its Place Classification Methods. and Analysis 145,Logistic regression 146, Multiclass logistic classifiers multinomial regression 150.
Formatting a dataset for classification problems 151. Learning pointwise updates with stochastic gradient descent 155. Jointly optimizing all parameters with second order methods 158. Fitting the model 162,Evaluating classification models 165. Strategies for improving classification models 169. Separating Nonlinear boundaries with Support vector machines 172. Fitting and SVM to the census data 174, Boosting combining small models to improve accuracy 177. Gradient boosted decision trees 177,Comparing classification methods 180. Case study fitting classifier models in pyspark 182. Summary 184, Chapter 6 Words and Pixels Working with Unstructured Data 185. Working with textual data 186,Cleaning textual data 186.
Extracting features from textual data 189, Using dimensionality reduction to simplify datasets 192. Principal component analysis 193,Latent Dirichlet Allocation 205. Using dimensionality reduction in predictive modeling 209. Images 209,Cleaning image data 210,Thresholding images to highlight objects 213. Dimensionality reduction for image analysis 216, Case Study Training a Recommender System in PySpark 220. Summary 222, Chapter 7 Learning from the Bottom Up Deep Networks and.
Unsupervised Features 223,Learning patterns with neural networks 224. A network of one the perceptron 224, Combining perceptrons a single layer neural network 226. Parameter fitting with back propagation 229,Table of Contents. Discriminative versus generative models 234,Vanishing gradients and explaining away 235. Pretraining belief networks 238,Using dropout to regularize networks 241.
Convolutional networks and rectified units 242,Compressing Data with autoencoder networks 246. Optimizing the learning rate 247,The TensorFlow library and digit recognition 249. The MNIST data 250,Constructing the network 252,Summary 256. Chapter 8 Sharing Models with Prediction Services 257. The architecture of a prediction service 258,Clients and making requests 260. The GET requests 260,The POST request 262,The HEAD request 262.
The PUT request 262,The DELETE request 263,Server the web traffic controller 263. Application the engine of the predictive services 265. Persisting information with database systems 266,Case study logistic regression service 267. Setting up the database 268,The web server 271,The web application 273. The flow of a prediction service training a model 274. On demand and bulk prediction 283,Summary 287,Chapter 9 Reporting and Testing Iterating on. Analytic Systems 289, Checking the health of models with diagnostics 290.
Evaluating changes in model performance 290,Changes in feature importance 294. Changes in unsupervised model performance 295,Iterating on models through A B testing 297. Experimental allocation assigning customers to experiments 298. Deciding a sample size 299,Multiple hypothesis testing 302. Table of Contents,Guidelines for communication 302. Translate terms to business values 303,Visualizing results 303.
Case Study building a reporting service 304,The report server 304. The report application 305,The visualization layer 306. Summary 310, In Mastering Predictive Analytics with Python you will work through a step by step. process to turn raw data into powerful insights Power packed with case studies and. code examples using popular open source Python libraries this volume illustrates. the complete development process for analytic applications The detailed examples. illustrate robust and scalable applications for common use cases You will learn to. quickly apply these methods to your own data,What this book covers. Chapter 1 From Data to Decisions Getting Started with Analytic Applications teaches. you to describe the core components of an analytic pipeline and the ways in which. they interact We also examine the differences between batch and streaming. processes and some use cases in which each type of application is well suited We. walk through examples of both basic applications using both paradigms and the. design decisions needed at each step, Chapter 2 Exploratory Data Analysis and Visualization in Python examines many of the.
tasks needed to start building analytical applications Using the IPython notebook. we ll cover how to load data in a file into a data frame in pandas rename columns. in the dataset filter unwanted rows convert types and create new columns In. addition we ll join data from different sources and perform some basic statistical. analyses using aggregations and pivots, Chapter 3 Finding Patterns in the Noise Clustering and Unsupervised Learning shows. you how to identify groups of similar items in a dataset It s an exploratory analysis. that we might frequently use as a first step in deciphering new datasets We explore. different ways of calculating the similarity between data points and describe what. kinds of data these metrics might best apply to We examine both divisive clustering. algorithms which split the data into smaller components starting from a single. group and agglomerative methods where every data point starts as its own cluster. Using a number of datasets we show examples where these algorithms will perform. better or worse and some ways to optimize them We also see our first small data. pipeline a clustering application in PySpark using streaming data. Chapter 4 Connecting the Dots with Models Regression Methods examines the fitting. of several regression models including transforming input variables to the correct. scale and accounting for categorical features correctly We fit and evaluate a linear. regression as well as regularized regression models We also examine the use of. tree based regression models and how to optimize parameter choices in fitting them. Finally we will look at a sample of random forest modeling using PySpark which. can be applied to larger datasets, Chapter 5 Putting Data in its Place Classification Methods and Analysis explains. how to use classification models and some of the strategies for improving model. performance In addition to transforming categorical features we look at the. interpretation of logistic regression accuracy using the ROC curve In an attempt. to improve model performance we demonstrate the use of SVMs Finally we will. achieve good performance on the test set through Gradient Boosted Decision Trees. Chapter 6 Words and Pixels Working with Unstructured Data examines complex. unstructured data Then we cover dimensionality reduction techniques such as. the HashingVectorizer matrix decompositions such as PCA CUR and NMR. and probabilistic models such as LDA We also examine image data including. normalization and thresholding operations and see how we can use dimensionality. reduction techniques to find common patterns among images. Chapter 7 Learning from the Bottom Up Deep Networks and Unsupervised Features. introduces deep neural networks as a way to generate models for complex data. types where features are difficult to engineer We ll examine how neural networks. are trained through back propagation and why additional layers make this. optimization intractable, Chapter 8 Sharing Models with Prediction Services describes the three components of. a basic prediction service and discusses how this design will allow us to share the. results of predictive modeling with other users or software systems.

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