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Analysis Study Research Paper | Computer Engineering | Volume 15 Issue 2, February 2026 | Pages: 6 - 8 | United States
Feature Engineering in Machine Learning: Techniques, Challenges, and Best Practices - A Comprehensive Review
Abstract: Feature engineering plays a vital role in enhancing the performance of machine learning models by transforming raw data into meaningful inputs. This paper explores the significance of feature engineering, discussing various techniques such as feature extraction, transformation, and selection. Additionally, it highlights the role of automation tools and their impact on improving efficiency in the model development process. Through a case study on the Titanic dataset, we demonstrate how feature engineering enhances predictive accuracy. The paper also examines challenges and best practices, offering insights into the future trends of feature engineering in machine learning.
Keywords: Feature Engineering, Machine Learning, Data Preprocessing, Predictive Modeling, Data Science
How to Cite?: Munisekhar Katta, "Feature Engineering in Machine Learning: Techniques, Challenges, and Best Practices - A Comprehensive Review", Volume 15 Issue 2, February 2026, International Journal of Science and Research (IJSR), Pages: 6-8, https://www.ijsr.net/getabstract.php?paperid=SR25223105328, DOI: https://dx.doi.org/10.21275/SR25223105328