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Cover of Predictive Analytics and Data Mining

Predictive Analytics and Data Mining

Written by Vijay Kotu,Bala Deshpande

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447 pages, about 9 hours of reading

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About this book

Put Predictive Analytics into ActionLearn 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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Reading guide

Themes, characters and key ideas in Predictive Analytics and Data Mining, written by Chaptra AI.

  • about 15 hours
  • intermediate
  • informative
  • practical
  • structured

Kotu and Deshpande's "Predictive Analytics and Data Mining" serves as an accessible introduction to the foundational concepts and practical applications of data science. The book systematically unpacks key methodologies, including regression, classification, clustering, and association rules, guiding readers through the entire analytical process from problem definition to model deployment. It emphasizes the business context and strategic value of analytics, bridging the gap between theoretical knowledge and real-world implementation. Designed for business professionals and aspiring data scientists, it provides a clear, concise overview without requiring deep prior mathematical expertise, focusing instead on intuitive understanding and practical utility.

"Predictive analytics is about extracting information from data and using it to predict trends and behavior patterns."

Key themes

Data-Driven Decision Making
This theme is central to the entire book, advocating for the transformation of raw data into actionable insights to guide strategic and operational decisions. The authors consistently highlight how predictive analytics enables businesses to move beyond intuition, using evidence to forecast trends, optimize processes, and gain competitive advantages. It's presented as a fundamental shift in organizational culture towards relying on empirical evidence.
The Analytical Process Lifecycle
The book meticulously outlines the systematic, iterative stages involved in a data mining project, from problem definition and data collection to model building, evaluation, and deployment. This theme underscores that successful analytics is not a one-off task but a structured, cyclical process requiring careful planning, execution, and refinement at each stage. It emphasizes the importance of a structured approach to ensure reliable and impactful results.
Ethical Considerations and Responsible AI
While not the primary focus, the book dedicates attention to the ethical implications of predictive analytics, particularly concerning data privacy, fairness, and the potential for bias in models. This theme encourages a thoughtful and responsible approach to data science, prompting readers to consider the societal impact of their analytical work and the importance of transparency and accountability in model development and deployment.

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