Data Mining with Python: Theory, Application, and Case Studies

Data mining is more than choosing an algorithm: useful analysis depends on how data is collected, joined, checked, prepared, and understood along the way. Di Wu’s Data Mining with Python: Theory, Application, and Case Studies follows that full process, pairing the concepts behind each stage with practical Python tutorials and case studies.

Rather than treating data preparation and analysis as separate worlds, the book builds from data wrangling toward methods for finding patterns and making predictions. It offers students, data scientists, and business analysts an applied route into the subject, with examples that make the steps of a data-mining workflow concrete.

Follow the data from collection to analysis

The first section covers the groundwork: collecting data from files, the web, SQL databases, and APIs; integrating sources; exploring data statistically; visualizing it; and preprocessing it. Topics include missing values, outliers, reduction, discretization, scaling, and data warehouses. This sequence helps readers see how early choices affect the analysis that follows.

Explore classification, regression, and more

The second section turns to data-analysis techniques, including classification, regression, clustering, frequent patterns, and outlier detection. The contents name methods such as nearest-neighbor classifiers, decision trees, support vector machines, Naive Bayes, logistic regression, regularization, cross-validation, and ensemble methods.

Theory with a practical purpose

Each topic is approached through three complementary questions: what a method is, why it matters in an application, and how it can be used in a case study. Tutorials and examples include familiar datasets and applied problems, giving readers material to work through as they connect ideas with Python-based practice.

A useful fit for applied learners

This book is suited to readers who want to understand data-mining concepts while following their implementation in Python. Its pipeline-based structure is especially relevant to students and practitioners who want to connect data preparation, visualization, and analysis into one coherent workflow.

For a hands-on introduction to the stages and methods of data mining, this CRC Press title brings the theory, application, and case-study perspective together in one book.