Decoding the Monkey Pox Outbreak: Unsupervised Machine Learning Using Decision Tree

The Monkeypox outbreak in Central London in 2022 has posed significant challenges for healthcare practitioners and public health authorities. That is why this research seeks to use unsupervised machine learning to analyze data from 25,000 infected patients. By taking advantage of clustering techniques, including k-means and hierarchical clustering, we will attempt to identify underlying structures and knowledge sources from the information. Ultimately, this work endeavors to provide new valuable information into the dynamics of the outbreak, including spread trends, geographical clustering, and demographic sources of risk. The results might prove invaluable for developing interventions and control measures in any future outbreaks of the monkeypox virus or other infectious diseases.