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"Predictive Analytics" is a foundational and practical guide to understanding how organizations use data to anticipate future outcomes and make informed decisions. This book blends theory, technical models, and real-world applications to help readers unlock the potential of data-driven forecasting.The book introduces readers to the science of prediction - the methods, tools, and processes used to analyze current and historical data to forecast future events. It aims to bridge the gap between data science theory and practical business use.A typical book on Predictive Analytics focuses on:Understanding Patterns in Data - How data from the past can be used to forecast future events.Statistical and Machine Learning Models - Explaining algorithms such as regression, decision trees, and neural networks that power predictions.Business Applications - Real-world use cases in industries like finance (credit risk), marketing (customer churn), healthcare (disease prediction), and manufacturing (predictive maintenance).Tools and Techniques - Guidance on using software like Python, R, SAS, or RapidMiner for building predictive models.