DDS-7255

Zheng, A., & Casari, A. (2018). Feature Engineering for Machine Learning. O’Reilly Media, Inc.

Chapters 2–4 provide a practical overview of feature engineering techniques, essential for enhancing machine learning model performance.

Géron, A. (2023, January 1). O’Reilly Book Club: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Third Edition). O’Reilly Media, Inc.

Chapter 4 explores advanced machine learning techniques using Scikit-Learn, Keras, and TensorFlow, offering hands-on guidance for practitioners.

Müller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python. O’Reilly Media, Inc.

Chapter 3 delves into practical applications of machine learning algorithms using Python, providing foundational knowledge for new learners.

Abdelouahed, S. M., Abla, R., Asmae, E., & Abdellah, A. (2024). Harnessing feature engineering to improve machine learning: A review of different data processing techniques. In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV), 1–6. https://doi.org/10.1109/ISCV60512.2024.10620105

This review highlights various feature engineering techniques that enhance machine learning performance, providing insights for researchers and practitioners.

Pritam, N., Malhotra, S., Aluvala, S., Gill, K. S., & Devliyal, S. (2024). Bankruptcy detection using random forest classifier and data balancing with SMOTE analysis. In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0, 1–4. https://doi.org/10.1109/OTCON60325.2024.10688030

This study combines random forest classifiers with SMOTE analysis for bankruptcy detection, showcasing innovative approaches to data balancing.

Sharma, V., Singh, A., Saxena, A. K., & Saxena, V. (2023). A logistic regression-based credit risk assessment using WoE binning and enhanced feature engineering approach ANOVA and Chi-Square. In 2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART), 499–507. https://doi.org/10.1109/SMART59791.2023.10428399

This paper explores credit risk assessment using logistic regression with WoE binning, ANOVA, and Chi-square, highlighting enhanced feature engineering techniques.