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This chapter provides a comprehensive exploration of major Python machine learning libraries through practical examples spanning diverse application domains. It demonstrates air quality prediction using LSTM, CNN, XGBoost, and Prophet models for CO concentration forecasting, highlighting comparative performance metrics and temporal pattern analysis. The entertainment industry analysis employs ARIMA forecasting, clustering, and recommendation systems on movie/TV show datasets to uncover production trends and audience preferences. Natural language processing capabilities are showcased using the Pattern library for tokenization, sentiment analysis, spelling correction, and text classification. Web scraping techniques with Scrapy illustrate automated data extraction from Wikipedia for knowledge discovery. Each example integrates multiple libraries including TensorFlow, scikit-learn, pandas, and matplotlib, emphasizing practical implementation strategies, data preprocessing pipelines, and visualization techniques for real-world machine learning applications.

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Introduction of Machine Learning Libraries in Python with Illustrative Examples

  • 章节

  • 发表日期：

    2026年

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  • Chunwei Zhang,
  • Tianpeng Li,
  • Ying Dai,
  • Li Sun,
  • Ardashir Mohammadzadeh

摘要

This chapter provides a comprehensive exploration of major Python machine learning libraries through practical examples spanning diverse application domains. It demonstrates air quality prediction using LSTM, CNN, XGBoost, and Prophet models for CO concentration forecasting, highlighting comparative performance metrics and temporal pattern analysis. The entertainment industry analysis employs ARIMA forecasting, clustering, and recommendation systems on movie/TV show datasets to uncover production trends and audience preferences. Natural language processing capabilities are showcased using the Pattern library for tokenization, sentiment analysis, spelling correction, and text classification. Web scraping techniques with Scrapy illustrate automated data extraction from Wikipedia for knowledge discovery. Each example integrates multiple libraries including TensorFlow, scikit-learn, pandas, and matplotlib, emphasizing practical implementation strategies, data preprocessing pipelines, and visualization techniques for real-world machine learning applications.