Climatic data analysis using machine learning and correlation with human health

Climatic data analysis and effects on human health is a data science project that focuses on the analysis and interpretation of climatic data to gain valuable insights into past and present climate patterns. The project utilises advanced data analytics techniques like regression models to process and analyse large-scale climatic datasets, enabling the identification of trends and patterns that contribute to a deeper understanding of climate dynamics. The primary objectives of this project are to investigate climate change phenomena, assess the impact of climatic change on human health, and predict the variation of spread of diseases as per the different climatic conditions. By employing various statistical models, machine learning algorithms, and visualisation tools, the project aims to uncover hidden relationships within the data and provide evidence-based findings for policymakers, researchers, and stakeholders. To achieve these goals, the project leverages diverse sources of climatic data, including maximum and minimum temperature records, rainfall and humidity measurements, atmospheric pressure data etc.