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Fundamentals of Machine Learning for Predictive Data by John D. Kelleher

By John D. Kelleher

Machine studying is frequently used to construct predictive versions via extracting styles from huge datasets. those types are utilized in predictive information analytics purposes together with rate prediction, probability evaluation, predicting purchaser habit, and record type. This introductory textbook bargains a close and centred remedy of crucial computing device studying methods utilized in predictive facts analytics, masking either theoretical techniques and useful functions. Technical and mathematical fabric is augmented with explanatory labored examples, and case experiences illustrate the applying of those versions within the broader company context.

After discussing the trajectory from facts to perception to selection, the e-book describes 4 ways to laptop studying: information-based studying, similarity-based studying, probability-based studying, and error-based studying. each one of those ways is brought by way of a nontechnical rationalization of the underlying proposal, through mathematical types and algorithms illustrated through designated labored examples. eventually, the booklet considers options for comparing prediction types and gives case reports that describe particular facts analytics tasks via each one section of improvement, from formulating the enterprise challenge to implementation of the analytics resolution. The ebook, proficient by means of the authors' decades of training computing device studying, and dealing on predictive facts analytics tasks, is acceptable to be used by way of undergraduates in machine technological know-how, engineering, arithmetic, or information; by way of graduate scholars in disciplines with functions for predictive information analytics; and as a reference for professionals.

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This book focuses on predictive data analytics, which is an important subfield of data analytics. 1 Predictive data analytics moving from data to insight to decision. 1 What Is Predictive Data Analytics? Applications of predictive data analytics include Price Prediction: Businesses such as hotel chains, airlines, and online retailers need to constantly adjust their prices in order to maximize returns based on factors such as seasonal changes, shifting customer demand, and the occurrence of special events.

Because a single consistent model cannot be found based on the sample training dataset alone, we say that machine learning is fundamentally an ill-posed problem. For example, if a new customer starts shopping at the supermarket and buys baby food, alcohol, and organic vegetables, our set of consistent models will contradict each other with respect to what prediction should be returned for this customer, for example, 2 will return GRP = single, 4 will return GRP = family, and 5 will return GRP = couple.

Using the CRISP-DM process improves the likelihood that predictive data analytics projects will be successful, and we recommend its use. In these discussions we do not refer to specific tools or implementations of these techniques. There are, however, many different, easy-to-use options for implementing machine learning models that interested readers can use to follow along with the examples in this book. We will look at application-based solutions first. Well-designed application-based, or point-andclick, tools make it very quick and easy to develop and evaluate models, and to perform associated data manipulation tasks.

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