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R: Predictive Analysis
by Tony Fischetti , Eric Mayor , Rui Miguel Forte
Product details
Pages: Kindle Edition
File Size: 34372 KB
Print pages 页数: 1581 pages
Publisher Finelybook 出版社: Packt Publishing; 1 edition (31 Mar. 2017)
Sold by 作者: Amazon Media EU S.à r.l.
Language 语言: English
ASIN: B0713SJV6X
Master the art of predictive modeling
About This Book
Load,wrangle,and analyze your data using the world's most powerful statistical programming language
Familiarize yourself with the most common data mining tools of R,such as k-means,hierarchical regression,linear regression,Naive Bayes,decision trees,text mining and so on.
We emphasize important concepts,such as the bias-variance trade-off and over-fitting,which are pervasive in predictive modeling
Who this book is for
If you work with data and want to become an expert in predictive analysis and modeling,then this Learning Path will serve you well. It is intended for budding and seasoned practitioners of predictive modeling alike. You should have basic knowledge of the use of R,although it's not necessary to put this Learning Path to great use.
What you will learn
Get to know the basics of R's syntax and major data structures
Write functions,load data,and install packages
Use different data sources in R and know how to interface with databases,and request and load JSON and XML
Identify the challenges and apply your knowledge about data analysis in R to imperfect real-world data
Predict the future with reasonably simple algorithms
Understand key data visualization and predictive analytic skills using R
Understand the language of models and the predictive modeling process
In Detail
Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines.
The power and domain-specificity of R allows the user to express complex analytics easily,quickly,and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R.
We start with an introduction to data analysis with R,and then gradually you'll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”,large data,communicating results,and facilitating reproducibility. You will then perform key predictive analytics tasks using R,such as train and test predictive models for classification and regression tasks,score new data sets and so on. By the end of this Learning Path,you will have explored and tested the most popular modeling techniques in use on real-world data sets and mastered a diverse range of techniques in predictive analytics.
This Learning Path combines some of the best that Packt has to offer in one complete,curated package. It includes content from the following Packt products:
Data Analysis with R,Tony Fischetti
Learning Predictive Analytics with R,Eric Mayor
Mastering Predictive Analytics with R,Rui Miguel Forte
Style and approach
Learn data analysis using engaging examples and fun exercises,and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course,which analyzes compelling data about life,health,and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course,but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of predictive modeling.
Contents
1. Module 1
1. RefresheR
2. The Shape of Data
3. Describing Relationships
4. Probability
5. Using Data to Reason About the World
6. Testing Hypotheses
7. Bayesian Methods
8. Predicting Continuous Variables
9. Predicting Categorical Variables
10. Sources of Data
11. Dealing with Messy Data
12. Dealing with Large Data
13. Reproducibility and Best Practices
2. Module 2
1. Visualizing and Manipulating Data Using R
2. Data Visualization with Lattice
3. Cluster Analysis
4. Agglomerative Clustering Using hclust()
5. Dimensionality Reduction with Principal Component Analysis
6. Exploring Association Rules with Apriori
7. Probability Distributions,Covariance,and Correlation
8. Linear Regression
9. Classification with k-Nearest Neighbors and Na?ve Bayes
10. Classification Trees
12. Multilevel Analyses
13. Text Analytics with R
14. Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML
3. Module 3
1. Gearing Up for Predictive Modeling
2. Linear Regression
3. Logistic Regression
4. Neural Networks
5. Support Vector Machines
6. Tree-based Methods
7. Ensemble Methods
8. Probabilistic Graphical Models
9. Time Series Analysis
10. Topic Modeling
11. Recommendation Systems
掌握预测模型的艺术
关于这本书
使用世界上最强大的统计编程语言加载，摆动和分析数据
熟悉R中最常见的数据挖掘工具，如k-means，层次回归，线性回归，朴素贝叶斯，决策树，文本挖掘等。
我们强调在预测建模中普遍存在的重要概念，如偏差方差折衷和过拟合
这本书是谁
如果您使用数据并希望成为预测分析和建模专家，那么此学习路径将为您服务。它是用于预测模型的萌芽和经验丰富的从业者。您应该具有使用R的基本知识，尽管没有必要将此学习路径用于极大的用途。
你会学到什么
了解R的语法和主要数据结构的基础知识
编写功能，加载数据和安装软件包
在R中使用不同的数据源，知道如何与数据库进行接口，并请求并加载JSON和XML
识别挑战并将您对R中数据分析的知识应用于不完美的现实世界数据
用相当简单的算法预测未来
使用R了解关键数据可视化和预测分析技能
了解模型语言和预测建模过程
详细
预测分析是一个使用数据构建预测未来感兴趣结果的模型的领域。它可以应用于一系列的业务策略，一直是搜索广告和推荐引擎的关键角色。
R的功能和领域特异性允许用户轻松，快速，简洁地表达复杂的分析。 R提供了一个免费的开源环境，非常适合在现实世界中学习和部署预测建模解决方案。这个学习路径将为您提供所有必要的步骤来掌握R的预测建模艺术。
我们首先介绍使用R进行数据分析，然后逐渐用预测建模来弄湿你的脚。您将掌握应用统计的基础知识，并利用这些知识来执行复杂而强大的分析。您将能够在实践中解决与执行数据分析相关的困难，并找到解决方案来处理“凌乱数据”，大数据，传达结果，并促进重现性。然后，您将使用R执行关键预测分析任务，例如用于分类和回归任务的训练和测试预测模型，评分新数据集等。在学习路径结束之前，您将探索并测试在现实世界数据集中使用的最流行的建模技术，并掌握了各种预测分析技术。
这个学习路径结合了Packt在一个完整的，策划的包中提供的一些最好的。它包含以下Packt产品的内容:
数据分析与R，托尼Fischetti
与R，Eric市长一起学习预测分析
掌握R，Rui Miguel Forte的预测分析
风格和方法
使用有趣的例子和有趣的练习来学习数据分析，并用温柔友好但全面的“逐步学习”方法。这是一个实践课程，借助教程分析有关生命，健康和死亡的令人信服的数据。它为您提供了一种解释本课程特有的数据的有用方法，但也可以应用于任何其他数据。本课程旨在成为超越预测建模基础的指南和参考。
目录
模块1
RefresheR
数据的形状
描述关系
概率
5.使用数据来理解世界
6.测试假设
贝叶斯方法
8.预测连续变量
9.预测分类变量
10.资料来源
处理凌乱数据
12.处理大数据
重现性和最佳实践
模块2
使用R可视化和操作数据
数据可视化与格子
聚类分析
使用hclust（）的聚集聚类
主成分分析的尺寸减少
探索与Apriori的联盟规则
概率分布，协方差和相关性
线性回归
分类与k最近的邻居和Na贝尔贝斯
分类树
多层次分析
13.使用R进行文本分析
14.使用插入和使用PMML导出预测模型的交叉验证和引导
3.模块3
预测性建模
线性回归
逻辑回归
神经网络
支持向量机
基于树的方法
7.集合方法
概率图形模型
9.时间序列分析
主题建模
建议系统
