Machine Learning with R Cookbook - Second Edition

Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code

About This Book

• Apply R to simplify predictive modeling with short and simple code
• Use machine learning to solve problems ranging from small to big data
• Build a training and testing dataset, applying different classification methods.

Who This Book Is For

This book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful.

What You Will Learn

• Create and inspect transaction datasets and perform association analysis with the Apriori algorithm
• Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm
• Compare differences between each regression method to discover how they solve problems
• Detect and impute missing values in air quality data
• Predict possible churn users with the classification approach
• Plot the autocorrelation function with time series analysis
• Use the Cox proportional hazards model for survival analysis
• Implement the clustering method to segment customer data
• Compress images with the dimension reduction method
• Incorporate R and Hadoop to solve machine learning problems on big data

In Detail

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.

Style and approach

This is an easy-to-follow guide packed with hands-on examples of machine learning tasks. Each topic includes step-by-step instructions on tackling difficulties faced when applying R to machine learning.

探索超過110個食譜，使用簡單易用的R程式碼分析數據並建立預測模型。

關於本書：
- 使用簡短而簡單的程式碼，應用R來簡化預測建模。
- 使用機器學習解決從小數據到大數據的問題。
- 建立訓練和測試數據集，應用不同的分類方法。

本書適合對象：
- 數據科學專業人士、數據分析師或已經使用R進行數據分析和機器學習的人，希望成為機器學習領域的專家。
- 希望提高機器學習模型效率並需要處理不同類型數據集的人，將會發現本書非常有見地。

你將學到的內容：
- 創建和檢查交易數據集，並使用Apriori算法進行關聯分析。
- 使用各種圖形可視化模式和關聯，並使用Eclat算法找到頻繁項集。
- 比較每種回歸方法之間的差異，以了解它們如何解決問題。
- 檢測和填補空氣質量數據中的缺失值。
- 使用分類方法預測可能的流失用戶。
- 使用時間序列分析繪製自相關函數。
- 使用Cox比例風險模型進行生存分析。
- 使用聚類方法對客戶數據進行分段。
- 使用降維方法壓縮圖像。
- 整合R和Hadoop來解決大數據上的機器學習問題。

詳細內容：
大數據已成為許多行業的熱門詞彙。越來越多的人接觸到這個詞彙，並且正在尋找如何利用大數據來改善銷售和盈利能力。然而，收集、聚合和可視化數據只是其中的一部分。從數據中提取有用信息是另一項任務，也是一項更具挑戰性的任務。《Machine Learning with R Cookbook, Second Edition》使用實用的方法教授如何使用R進行機器學習。每個章節都分為幾個簡單的食譜。通過每個食譜中提供的逐步指示，您將能夠使用各種機器學習套件構建預測模型。在本書中，您將首先學習如何設置R環境並使用簡單的R命令探索數據。下一個主題涵蓋了如何使用機器學習分析進行統計分析，以及在本書後面詳細介紹的評估創建的模型。您還將學習如何將R和Hadoop集成，創建一個大數據分析平台。詳細的插圖提供了開始應用機器學習到個別項目所需的所有信息。使用《Machine Learning with R Cookbook》，機器學習變得更加容易。

風格和方法：
這是一本易於遵循的指南，充滿了機器學習任務的實際示例。每個主題都包含了使用R進行機器學習時遇到的困難的逐步指示。