Enhancing Data Analytics Performance Using Optimized Machine Learning Pipelines: A Case Study in Predictive Modeling

Gery Jisky, Korlin Saan

Abstract


The increasing complexity of data in various industries necessitates efficient machine learning pipelines to enhance predictive modeling accuracy and performance. This study presents an optimized machine learning pipeline that integrates data preprocessing, feature selection, and hyperparameter tuning. Using real-world datasets from healthcare and e-commerce domains, the proposed pipeline demonstrates significant improvements in model performance compared to traditional methods. Results indicate that systematic optimization at each stage of the pipeline can lead to increased predictive accuracy and reduced computational overhead.

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