Description
Book Synopsis.- Effects of Imputation Techniques on Predictive Performance of Supervised Machine Learning Algorithms: Empirical Insights from Health Data Classification.
.- Predicting Air Quality in an Urban African City Using Four Comparative Novel Time Series Models.
.- Obesity Classification Using Weighted Hard and Soft Voting Ensemble Machine Learning Classifiers.
.- Predictive Modeling for Disease Diagnosis Using Calibrated Algorithms: A Comparative Study.
.- Predicting Precipitation Dynamics in Africa Using Deep Learning Models.
.- Enhancing Predictive Performance through Optimized Ensemble Stacking for Imbalanced Classification Problems.
.- A Comparative Exploration of SHAP and LIME for Enhancing the Interpretability of Machine Learning Models in BMI Classification.
.- Decision Tree Planning Strategies for Predicting Obesity.
.- Clustering Multiple Time Series with SSA.
.- Spine-Based Calibration for Classification Algorithms: An Experimental Comparison of Various Imbalanced Ratios.
.- Exploring the Applicability of Advanced Exponential Smoothing and NN Models for Climate Time Series Forecasting: Insights and Changepoint Prediction in the Brazilian Context.
.- A Comprehensive Forecasting Experiment on Temperature Trends Across Thirty-Two American Countries.
.- A Comparative Analysis of Sampling Methods for Imbalanced Data Classification in Machine Learning Health Applications.
.- Comparative Analysis of MCC, F1-Score, and Balanced Accuracy Metrics for Imbalanced Health Data Classification.
.- Basics of R- Shiny for developing Interactive Visualizations.
