Paper Title
PREDICTIVE MODELING FOR HUMAN MONKEYPOX DETECTION USING SYMPTOMATIC DATASET

Abstract
This project explores the application of supervised machine learning models to predict the occurrence of Monkeypox, a viral zoonotic disease. A dataset containing features related to potential risk factors is used in supervised models to predict the likelihood of Monkeypox infection. The project encompasses data preprocessing, feature engineering, and model evaluation to develop robust predictive models. Results from supervised approaches contribute to a comprehensive understanding of Monkeypox dynamics and provide valuable insights for public health interventions and surveillance strategies.