Put Predictive Analytics into Action Learn the basics of Predictive
Analysis and Data Mining through an easy to understand conceptual
framework and immediately practice the concepts learned using the
open source RapidMiner tool. Whether you are brand new to Data
Mining or working on your tenth project, this book will show you
how to analyze data, uncover hidden patterns and relationships to
aid important decisions and predictions. Data Mining has become an
essential tool for any enterprise that collects, stores and
processes data as part of its operations. This book is ideal for
business users, data analysts, business analysts, business
intelligence and data warehousing professionals and for anyone who
wants to learn Data Mining. You'll be able to: 1. Gain the
necessary knowledge of different data mining techniques, so that
you can select the right technique for a given data problem and
create a general purpose analytics process. 2. Get up and running
fast with more than two dozen commonly used powerful algorithms for
predictive analytics using practical use cases. 3. Implement a
simple step-by-step process for predicting an outcome or
discovering hidden relationships from the data using RapidMiner, an
open source GUI based data mining tool Predictive analytics and
Data Mining techniques covered: Exploratory Data Analysis,
Visualization, Decision trees, Rule induction, k-Nearest Neighbors,
Naive Bayesian, Artificial Neural Networks, Support Vector
machines, Ensemble models, Bagging, Boosting, Random Forests,
Linear regression, Logistic regression, Association analysis using
Apriori and FP Growth, K-Means clustering, Density based
clustering, Self Organizing Maps, Text Mining, Time series
forecasting, Anomaly detection and Feature selection.
Implementation files can be downloaded from the book companion site
at www.LearnPredictiveAnalytics.com
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