摘要
在大数据和人工智能飞速发展的今天,数据已成为重要的生产资料.然而,数据质量直接影响到数据分析和机器学习模型的效果.数据处理与特征工程作为数据科学的核心步骤,是将原始数据转化为高质量输入数据的关键过程.Python语言因其简洁、强大和广泛的第三方库支持,成为数据处理与特征工程的首选工具.对Python语言在数据处理与特征工程中的应用方法进行了系统探讨,涵盖数据无量纲化、缺失值处理、分类特征处理、连续特征处理以及特征选择等方面.通过提供代码示例,为数据科学研究提供了实用的参考,有助于从业者提升数据分析和机器学习模型的性能.
Abstract
In today's era of rapid development in big data and artificial intelligence,data has become an essential production resource.However,the quality of data directly affects the outcome of data analysis and machine learning models.Data processing and feature engineering,as core steps in data science,are critical processes that transform raw data into high-quality input data.Due to its simplicity,power,and extensive third-party library support,Python has become the preferred tool for data processing and feature engineering.This paper systematically explores the application methods of Python in data processing and feature engineer-ing,covering areas such as data normalization,missing value handling,categorical feature processing,continuous feature process-ing,and feature selection.By providing code examples,this study offers practical references for data science research and helps practitioners improve the performance of data analysis and machine learning models.
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