Using gradient boosting, it assesses the effectiveness of many machine learning algorithms on three datasets: water potability, diabetes, and heart disease. Its goal is to evaluate these models’ ability to forecast various environmental and health events. Since every dataset presents unique difficulties, a thorough understanding of the algorithms’ advantages and disadvantages is possible. The goal of this study’s introduction of Enhanced Gradient Boosting is to advance the field by improving prediction accuracy. The study’s enhanced method will overcome the drawbacks of the traditional Gradient Boosting approach in managing the complexities present in diverse datasets. Standard Gradient Boosting performs poorly on the water potability dataset, particularly when it comes to class separation. For example, class 0 and class 1 model precisions are equivalent to 0.66 and 0.64, respectively, with recall rates of 0.93 and 0.20 and F1-scores of 0.78 and 0.31. The typical Gradient Boosting model performed well, with an accuracy of 0.66, on both the diabetes and heart disease datasets. On the other hand, the new method outperformed the old one, particularly when handling the noisy water potability data. An organized method that includes the following phases is used to construct the Enhanced Gradient Boosting model: data collecting, data preprocessing, EDA, data splitting, and scaling. This end-to-end method shows how creative algorithm creation combined with comparison analysis may lead to tailored machine learning solutions. These findings show how well the algorithm performed on the two datasets and highlight how versatile Gradient Boosting is for solving various prediction issues. The discovered results have great significance not only for applications related to medical diagnostics and environmental monitoring, but also for paving the way for future advancements within the machine learning research framework.

Gradient Boosting Machine Learning Predictive Modelling Healthcare Diagnostics Environmental Monitoring