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Autor(en): 
  • Justin Benedict
  • Python Scripts for Feature Engineering: Master Data Preparation: Foundations, Data Preparation, Feature Creation, and Dimensionality Reduction 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 14-24 Tagen versandfertig
    Veröffentlichung:  Juni 2026  
    Genre:  EDV / Informatik 
     
    COMPUTERS / Programming / Algorithms
    ISBN:  9798181849655 
    EAN-Code: 
    9798181849655 
    Verlag:  Independently Published 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 279 mm / B 216 mm / D 21 mm 
    Gewicht:  920 gr 
    Seiten:  398 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:

    Stop wasting hours cleaning messy datasets before they derail your machine learning projects
    Python Scripts for Feature Engineering: Master Data Preparation gives you a practical, code driven framework for transforming raw data into high quality features ready for analytics and machine learning. Instead of relying on theory heavy explanations, this book focuses on real world workflows, reusable Python scripts, and proven techniques used throughout the data preparation process.
    Learn to clean inconsistent datasets, engineer meaningful features, and automate preparation pipelines using Pandas, NumPy, and Scikit Learn. Every chapter is designed to help you move from raw data to model ready datasets with greater speed, accuracy, and confidence.
    Build cleaner datasets and reduce data preparation time through repeatable workflows
    Master feature creation, transformation, encoding, scaling, and dimensionality reduction techniques
    Develop production ready data preparation pipelines that improve scalability and maintainability
    Written for data scientists, machine learning engineers, analysts, developers, and technical professionals who need reliable feature engineering solutions in real world environments. Based on practical implementation experience and industry standard workflows used in modern data science projects.
    You'll gain the exact techniques needed to prepare high quality data before poor feature design undermines your results

      
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