Description

This Master Prompt provides a comprehensive methodological framework for developing data-driven predictive models using classical and advanced machine learning algorithms across multidisciplinary research applications. It guides researchers through supervised learning approaches for regression and classification, including decision trees, random forests, support vector machines, support vector regression, k-nearest neighbours, gradient boosting, extreme gradient boosting, Gaussian process regression and other relevant learning algorithms. The theme covers dataset construction, exploratory data analysis, missing-value treatment, outlier assessment, data normalization, categorical encoding, feature extraction, feature transformation, feature selection, dimensionality reduction, class imbalance management and prevention of data leakage. Particular attention is given to model selection, training–validation–testing strategies, hyperparameter optimization, grid search, randomized search, Bayesian optimization, Optuna-based optimization and computational efficiency. Researchers are guided in comparing machine learning models against statistical baselines, evaluating model complexity, diagnosing underfitting and overfitting, interpreting predictor contributions and establishing reproducible computational workflows. The theme integrates applications represented in the uploaded literature, including nanofiltration performance, material properties, environmental pollutant formation, pharmaceutical processes, property valuation, thermal behaviour, mechanical strength, geological prediction and clinical outcomes. It emphasizes scientifically justified algorithm selection, robust performance evaluation, reproducibility, generalization and the translation of machine learning predictions into meaningful scientific or engineering knowledge.