Research on data processing and feature engineering methods based on Python
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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