• Mrs.Sathya T
• Ms.Deeksha S
• Ms.Gayathri S
• Ms.Kavya G
• Ms.Divya N

This project leverages machine learning to predict disease outbreaks by analyzing data from social media platforms like Twitter, Facebook, and Reddit, as well as online news sources. Utilizing algorithms such as Logistic Regression for binary classification of outbreak vs. non-outbreak scenarios, Naive Bayes for handling large-scale, diverse data, Support Vector Machines for identifying complex patterns in the data, Random Forest for improving prediction accuracy with multiple decision trees, and K-Means Clustering for grouping similar data points, the system processes unstructured data to identify early signals of potential outbreaks. The project provides timely insights and visualizations, enabling public health officials to forecast and respond to emerging threats more effectively. By integrating diverse real-time data sources, this approach enhances early detection and contributes to a better understanding of disease dynamics. Its adaptability allows the system to refine predictions as new data emerges, while its scalability ensures deployment across various regions. This innovative use of technology not only improves public health surveillance but also facilitates more informed decision-making, helping to mitigate the impact of future pandemics and enhance global health security.

The Smart Disease Outbreak Forecasting project uses machine learning to predict disease outbreaks by analysing data from social media and real-time sources. It employs algorithms like Logistic Regression, Naive Bayes, and Random Forest to process unstructured data, providing timely insights for public health officials. Key features include sentiment analysis using Text Blob, hyperparameter tuning via Grid Search CV, and a Flask API for real-time predictions, all packaged with Docker for consistent deployment. This approach enhances public health surveillance and decision-making by integrating diverse data sources and offering actionable insights to manage disease spread effectively.

"SMART DISEASE OUTBREAK FORECASTING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.f225-f227, October-2024, Available :http://www.jetir.org/papers/JETIR2410519.pdf

"SMART DISEASE OUTBREAK FORECASTING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppf225-f227, October-2024, Available at : http://www.jetir.org/papers/JETIR2410519.pdf