Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner

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9780128014608: Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner

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, Naïve 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



  • Demystifies data mining concepts with easy to understand language
  • Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis
  • Explains the process of using open source RapidMiner tools
  • Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics
  • Includes practical use cases and examples

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Product Description:

Data Mining has become an essential tool for any enterprise as vast amounts of data are gathered leading to what is now being popularly called "Big Data". Data is also termed as the new oil, and companies which know how to process/refine and harness this data are the ones which will thrive. Data Science or data mining is the art and science of finding useful patterns in the data and making it actionable. These patterns generate new insights and allow us to convert future uncertainties into actionable probabilities. Whether you are brand new to data mining or working on your tenth predictive analytics project, Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner will show you how to analyze data and uncover hidden patterns and relationships to aid important decisions. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Vijay Kotu and Bala Deshpande show you the keys to mastering predictive analysis. You'll be able to: * Get a comprehensive understanding of different data mining techniques, be prepared to select the right technique for a given data problem and be ready to create a general purpose analytics process.* Use RapidMiner, an open source, GUI based data mining tool to create and develop your own data mining processes without having to write complicated lines of programming code.* Get up and running fast with 20 commonly used powerful techniques for predictive analysis* Implement a simple 5 step process for implementing algorithms that can be used for performing predictive analytics. * Demystifies data mining concepts with easy to understand language* Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis* Explains the process of using open source RapidMiner tools* Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics* Includes practical use cases and examples

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Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining thro....

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Vijay Kotu, Bala Deshpande
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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Book Condition: New. 236 x 193 mm. Language: English . Brand New Book. 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. Bookseller Inventory # AA59780128014608

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Vijay Kotu, Bala Deshpande
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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Book Condition: New. 236 x 193 mm. Language: English . Brand New Book. 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. Bookseller Inventory # AA59780128014608

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Book Description Paperback. Book Condition: New. Not Signed; 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 proje. book. Bookseller Inventory # ria9780128014608_rkm

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