Exploring Advanced Techniques and Strategies for Python in Machine Learning Applications

Qiwei Yang

doi:10.6911/WSRJ.202409_10(9).0002

Authors

Qiwei Yang

DOI:

https://doi.org/10.6911/WSRJ.202409_10(9).0002

Keywords:

Python; Machine Learning; Techniques and Strategies; Data Preprocessing; Feature Engineering.

Abstract

This article delves deep into the application techniques and strategies of Python in the field of machine learning. It covers various aspects such as data preprocessing, feature engineering, model selection and evaluation, hyperparameter tuning, and addresses issues like overfitting, underfitting, and high time costs. Proposed solutions include adjusting model complexity, data augmentation, and building automated feature engineering systems. The effectiveness of Python-based machine learning techniques is validated through case studies in image classification and stock price prediction. While Python offers efficient tools for machine learning, challenges still exist at the data, model, and algorithm levels, requiring ongoing research in automated and interpretable machine learning systems to make advancements.

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