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 [external URL]
- 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
Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.
- Data Mining Process
- Data Exploration
- Model Evaluation
- Text Mining
- Time Series
- Anomaly Detection
- Advanced Data Mining
- Getting Started with RapidMiner
Vijay Kotu is Vice President of Analytics at ServiceNow. He leads the implementation of large-scale data platforms and services to support the company's enterprise business. He has led analytics organizations for over a decade with focus on data strategy, business intelligence, machine learning, experimentation, engineering, enterprise adoption, and building analytics talent. Prior to joining ServiceNow, he was Vice President of Analytics at Yahoo. He worked at Life Technologies and Adteractive where he led marketing analytics, created algorithms to optimize online purchasing behavior, and developed data platforms to manage marketing campaigns. He is a member of the Association of Computing Machinery and a member of the Advisory Board at RapidMiner.
Dr. Deshpande has extensive experience in working with companies ranging from startups to Fortune 5 in fields ranging from automotive, aerospace, retail, food, and manufacturing verticals delivering business analysis; designing and developing custom data products for implementing business intelligence, data science, and predictive analytics solutions. He was the Founder of SimaFore, a predictive analytics consulting company which was acquired by Soliton Inc., a provider of testing solutions for the semiconductor industry. He was also the Founding Co-chair of the annual Predictive Analytics World-Manufacturing conference. In his professional career he has worked with Ford Motor Company on their product development, with IBM at their IBM Watson Center of Competence, and with Domino's Pizza at their data science and artificial intelligence groups. He has a Ph.D. from Carnegie Mellon and an MBA from Ross School of Business, Michigan.