Prediction of PM2.5 level in air using machine learning Available to Purchase

This project presents a comprehensive system integrating full-stack development with machine learning capabilities to forecast PM2.5 levels in the air. Leveraging the MEAN stack for robust full-stack development and the Prophet algorithm for machine learning, the system facilitates efficient prediction and visualization of PM2.5 concentrations. Upon accessing the system, users encounter an authentication page to ensure secure access. Following successful login, users are directed to the home page, which serves as the central interface for interaction. The home page features a user-friendly interface, including a dedicated section for data input. Users upload a CSV file containing the dataset and specify the prediction timeframe, choosing from options such as day, week, month, or year, along with entering the numerical duration. Subsequently, the machine learning algorithm processes the input data, generating predictions for the specified time-period. The system visualizes the forecasted PM2.5 levels through an interactive graph, providing users with intuitive insights into air quality trends. This visualization empowers users to make informed decisions and take proactive measures based on predicted PM2.5 concentrations. By seamlessly integrating full-stack development with machine learning, this project offers a practical solution for forecasting PM2.5 levels.