The Beijing AQI Predictor is a machine learning and deep learning project focused on forecasting the Air Quality Index (AQI) for Beijing. By analyzing historical air quality and meteorological data, this tool predicts future AQI levels to assist in air quality management and public awareness.
- Historical Data Analysis: Analyzes historical air quality and meteorological data.
- Predictive Modeling: Employs machine learning algorithms to forecast AQI levels.
- Data Visualization: Visualizes historical trends and future predictions of AQI.
- Forecasting: Generates future AQI forecasts based on trained models.
PM2.5 readings are commonly used to assess air pollution. PM2.5 refers to particulate matter with a diameter of less than 2.5 micrometers, which is a key indicator of air quality and pollution levels.
This dataset includes hourly air pollutants data from 12 nationally-controlled air-quality monitoring sites across Beijing. The data comprises:
- Air-Quality Data: Provided by the Beijing Municipal Environmental Monitoring Center.
- Meteorological Data: Matched with the nearest weather station from the China Meteorological Administration.
The dataset covers the period from March 1st, 2013 to February 28th, 2017.
- Zhang, S., Guo, B., Dong, A., He, J., Xu, Z., & Chen, S.X. (2017). Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457.
- The dataset files were downloaded from the UCI Machine Learning Repository and have not been modified.
To set up the project, follow these steps:
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Clone the repository:
git clone https://github.com/RamSharma06/Beijing-AQI-Predictor.git cd Beijing-AQI-Predictor