Air Quality Index Forecasting using Prophet(forecasting) python :

This repository contains code and documentation for forecasting the Air Quality Index (AQI) using the Prophet model. The project aims to predict future AQI values based on historical data, helping to understand air quality trends and make informed decisions.

Introduction Air Quality Index (AQI) is an essential metric used to communicate the quality of air to the public. It provides information about the level of pollution in the air and its potential impact on human health. Forecasting AQI helps in predicting air pollution levels, enabling proactive measures to reduce exposure to harmful pollutants. This project uses Facebook's Prophet model, a forecasting tool designed to handle time series data with strong seasonal effects and other patterns, to predict future AQI levels.

Modeling The forecasting model is built using Prophet, which is well-suited for time series forecasting with daily data. The model is trained on historical AQI data and then used to predict future AQI levels.

Prophet Model Prophet is an open-source tool developed by Facebook that allows for accurate and efficient forecasting of time series data. It is particularly adept at handling:

Missing data Outliers Strong seasonal patterns Model Training and Forecasting To train the model:

Load and preprocess the data. Initialize the Prophet model. Fit the model to the historical AQI data. Make future predictions using the fitted model. Evaluation Evaluate the model's performance using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to assess its predictive accuracy

Results The forecasted AQI values, along with visualizations, will provide insights into expected air quality trends. These results can be used to inform policy-making, public health advisories, and other strategic initiatives to manage air quality.