Python Data Mining: A Beginner's Guide with Practical Cases

This is the Chinese translation of Learning Data Mining with Python, aimed at readers who want to complete data mining tasks in Python but lack a systematic introduction. The book has 12 chapters, each built around a concrete case: first using decision trees and random forests to predict the outcome of NBA games, introducing the basic workflow of classification and ensemble learning; then implementing movie recommendations with affinity analysis and the Apriori algorithm; next come feature extraction and transformers, naive Bayes applied to social media text, graph mining to find interesting people to follow, cracking CAPTCHAs with neural networks, identifying document authors with function words and character n-grams, clustering news articles, and finally deep learning for image classification and big-data processing with MapReduce. The author adopts a "run the example first, then explain the principle" approach, working with common libraries such as scikit-learn, NumPy, pandas, NetworkX and Keras to thread data cleaning, feature engineering, modeling, evaluation and tuning into a complete pipeline; each chapter ends with a summary and additional directions for practice, helping readers move from imitating code to solving real problems independently.