Advances in Data Processing, Machine Learning, and Data Security

Abstract

This study examines how data processing, sophisticated machine learning (ML), and data security are crucial to data-driven decision-making. It covers accurate data collecting, cleaning, and pre-processing methods, which are the foundation for trustworthy ML models. Exploratory data analysis and feature engineering provide difficult dataset insights. Data quality depends on how missing data and outliers are handled. Predictive modelling uses ML approaches such supervised, unsupervised, semi-supervised, ensemble, and deep learning. Reducing dimensionality and selecting features improve model efficiency and interpretation. The model creation, training, and assessment methods ensure performance quality. The study also emphasises data security, ML ethics, privacy, and justice. New technologies in eCommerce data security are revolutionising protection methods, and AI-based solutions are moving cybersecurity towards transparency and confidentiality. However, future study will examine ethical problems and explainability to improve data-driven applications.