R Data Mining: Implement data mining techniques through practical use cases and real world datasets
关于此电子书
If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required.
What You Will LearnMaster relevant packages such as dplyr, ggplot2 and so on for data miningLearn how to effectively organize a data mining project through the CRISP-DM methodologyImplement data cleaning and validation tasks to get your data ready for data mining activitiesExecute Exploratory Data Analysis both the numerical and the graphical wayDevelop simple and multiple regression models along with logistic regressionApply basic ensemble learning techniques to join together results from different data mining modelsPerform text mining analysis from unstructured pdf files and textual dataProduce reports to effectively communicate objectives, methods, and insights of your analysesIn DetailR is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R.
It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques.
While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data.
Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
Style and approachThis book takes a practical, step-by-step approach to explain the concepts of data mining. Practical use-cases involving real-world datasets are used throughout the book to clearly explain theoretical concepts.
作者简介
Andrea Cirillo is currently working as an audit quantitative analyst at Intesa Sanpaolo Banking Group. He gained financial and external audit experience at Deloitte Touche Tohmatsu and internal audit experience at FNM, a listed Italian company. His main responsibilities involve the evaluation of credit risk management models and their enhancement, mainly within the field of the Basel III capital agreement. He is married to Francesca and is the father of Tommaso, Gianna, Zaccaria, and Filippo. Andrea has written and contributed to a few useful R packages such as updateR, ramazon, and paletteR, and regularly shares insightful advice and tutorials on R programming. His research and work mainly focus on the use of R in the fields of risk management and fraud detection, largely by modeling custom algorithms and developing interactive applications. Andrea has previously authored RStudio for R Statistical Computing Cookbook for Packt Publishing.
