Python 3 and Feature Engineering (True PDF)

English | 2023 | ISBN: 1683929497 | 229 pages | True PDF | 10.67 MB

This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, youll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where youll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework. FEATURESIncludes numerous practical examples and partial code blocks that illuminate the path from theory to applicationExplores everything from data cleaning to the subtleties of feature selection and extraction, covering a wide spectrum of feature engineering topicsOffers an appendix on working with the awk command-line utilityFeatures companion files available for downloading with source code, datasets, and figures