Paper Title
Predictive Modeling of Whale Health in Contaminated Marine Ecosystems Using Data-Driven Techniques

Abstract
The increasing levels of harmful contaminants in marine ecosystems, such as heavy metals (such as mercury and lead), organic pollutants (like PCBs and PAHs), and superfluous nutrients, are posing a growing threat to endangered whale populations. These pollutants have the potential to result in severe health problems in whales, including neurological injury, reproductive failure, and immune system degradation. Nevertheless, the precise relationship between whale health and specific contaminants is not yet completely understood, which makes it challenging to predict health outcomes and implement effective conservation strategies. This project aims to bridge that gap by developing a predictive model using Support Vector Machines (SVM), to establish correlations between whale health and various waterborne contaminants. By collecting and preprocessing environmental data on pollutants and whale health indicators, the model will analyze the data to forecast potential health hazards. This predictive analysis will facilitate early detection of pollution-related health risks, empowering conservation efforts. Additionally, the model will serve as a valuable tool for researchers, policymakers, and marine conservationists, helping to mitigate the impact of marine pollution on these critical marine species. Keywords- Whale Health, Water Quality, Marine Pollution, Waterborne Contaminants, Heavy Metals,