没有合适的资源？快使用搜索试试~ 我知道了~

首页Mastering-Predictive-Analytics-with-Python.pdf.pdf

Mastering-Predictive-Analytics-with-Python.pdf.pdf

综合文档

需积分: 10 111 浏览量更新于2023-05-28 评论 1 收藏 6.76MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

试读

323页

Mastering-Predictive-Analytics-with-Python.pdf

资源详情

资源评论

资源推荐

Mastering Predictive Analytics

with Python

Exploit the power of data in your business by building

advanced predictive modeling applications with Python

Joseph Babcock

Mastering Predictive Analytics with Python

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

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, ltering, mapping, and pivoting 33

Charting with Matplotlib 38

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

Afnity 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 tting and evaluation 114

Statistical signicance 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

Scaling out with PySpark – predicting year of song release 141

Summary 143

Chapter 5: Putting Data in its Place – Classication Methods

and Analysis 145

Logistic regression 146

Multiclass logistic classiers: multinomial regression 150

Formatting a dataset for classication problems 151

Learning pointwise updates with stochastic gradient descent 155

Jointly optimizing all parameters with second-order methods 158

Fitting the model 162

Evaluating classication models 165

Strategies for improving classication 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 classication methods 180

Case study: tting classier 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 tting with back-propagation 229

欢迎加入非盈利Python编程学习交流QQ群783462347，群里免费提供500+本Python书籍！

剩余322页未读，继续阅读

Mastering Predictive Analytics with Python

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

python-algorithms-mastering-basic-algorithms-in-the-python-language.9781430232377.53502

python-algorithms-mastering-basic-algorithms-in-the-python-language.9781430232377.53502.pdf

weixin_38743737

粉丝: 370
资源: 2万+

上传资源快速赚钱

我的内容管理收起

我的资源快来上传第一个资源

我的收益

登录查看自己的收益

我的积分登录查看自己的积分

我的C币登录后查看C币余额

我的收藏

我的下载

下载帮助

会员权益专享

Mastering-Predictive-Analytics-with-Python.pdf.pdf

评论0

会员权益专享

最新资源

Mastering-Predictive-Analytics-with-Python.pdf.pdf

评论0

Mastering Predictive Analytics with Python

python-algorithms-mastering-basic-algorithms-in-the-python-language.9781430232377.53502

Python: Advanced Predictive Analytics: 2017-12 python重量级高级大数据分析预测

下载Mastering Machine Learning with Python in Six Steps 英文高清完整.pdf 版

Mastering MongoDB 4.x - Second Edition.pdf

mastering-spring-cloud2018

mastering-cmake-

A Practical Implementation Guide to Predictive Data Analytics Using Python

Mastering Python [2016]

Mastering Python 原版PDF by Hattem

Daniel Arbuckle's Mastering Python

Mastering Python Forensics 无水印pdf 0分

Mastering Python Data Analysis 无水印pdf 0分

Expert Python Programming-3rd edition (2019).pdf

Mastering Machine Learning with Python in Six Steps 无水印pdf

R.for.Programmers.Mastering.the.Tools.14987368

Mastering Python Design Patterns 无水印pdf

Mastering Python Regular Expressions 无水印pdf

会员权益专享

最新资源