Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner

Easy explanation and introduction to Machine Learnig with a clarifying examples.

Superb easy to read book. Algorithms and rapid miner explained in straight forward fashion. Great book

This book is practical guide to realize data mining processes using rapid tool without programming, only click and drop. This includes a lot of figures and tables to help reader understand the algorithms and processors. On each chapter short column to introduce the key concept and typical algorithms using historical use cases for data miners and machine learning practitioners.This book covers two dozens of typical algorithms, i.e. classification: Decision Tree, Naive Bayes, Artificial Neural Network, Support Vector Machine, regression: Linear & Logistic Regression, association analysis: Frequent Pattern Growth, clustering: k-means, Density Base Spatial Clustering Application with Noise(DBSCAN), Self-Organizing Map, anomaly detection: Distance & Density base, Local Outlier and feature selection: Principal Component Analysis(PCA), Forward selection, Backward Elimination and so forth.This book shows how to implement these algorithms step by step among data mining process such as data handling, transforming, modeling and evaluation using powerful open source under ten thousands data examples: RapidMiner Studio Free version. It is possible to extend big data for millions of examples and multi-processors for rapid execution time to get the Middle and Large commercial version.

I'm not a full convert to the 'Big Data' religion, but Kotu and Deshpande show how to extract insight and meaning from large data sets, in ways that make sense to me. The book is full of worked examples, is perfect for an novice and math challenged person such as myself. I'm sure experienced data analysts will also appreciate the deep knowledge and experience the authors bring to the subject, and will ;find new, different and better ways to tackle some thorny problems.Also notable are Vijay Kotu's credentials: he does this for a living at Yahoo!, is clearly a master practitioner in this new field.Highly recommended for anyone grappling with ecommerce, wanting to understand why and how to maximize conversions from browsing to buying.5 stars.

This book does a nice job of explaining data mining concepts and predictive analytics. The main tool software tool they use is RapidMiner. Curiously RapidMiner was only introduced in chapter 13, the last chapter, although the authors mention you may want to read this chapter first.Some aspects of this book are done really nicely, like when they explain the Iris data set, which is a famous data set of flowers. There is a diagram (pg. 39) in the book showing the flower, and which labels showing which part of the flower is sepal length and which is petal length. I was familiar with the Iris data set before this book, but the diagram taught me what the sepal length meant exactly.There are lots of diagrams throughout the book which show different ways to visualize data and most concepts have clear explanations. The balance was reasonable between explaining techniques and showing how they are used.I would have liked to see more application of some of the concepts and RapidMiner specifically. Overall, this book does a good job of explaining the concepts and techniques of data mining.