A Self-Adaptive Data Preprocessing Pipeline for Machine Learning: An Automated and Dynamic Approach

The optimization and generalization of performance of a machine learning model is profoundly influenced by efficient data preprocessing. A machine's learning model does not perform to its expectable capability because the traditional automation processes which are manual rule-triggered are not data diverse friendly and rely heavily on human input. In this paper, we propose the Self-Adaptive Data Preprocessing Pipeline which modifies its preprocessing phases according to specific dataset details. Automated feature selection, data imputation, normalization, and noise reduction are performed using a blend of machine learning heuristics, statistics, and reinforcement learning. Through iterative assessment of various preprocessing methods, SADPP enhances pipeline processes in real time—streamlining human input, enhancing adaptive functions, and increasing reliability. The results indicate that SADPP achieves comparable accuracy while minimizing human tuning time by 85 % and computational resources by 22 %. This adaptive framework offers a significant stride towards sophisticated, automated, scalable data preprocessing systems in machine learning optimized for the growth of AI technologies.