PM is the abbreviation for Particulate Matter and the 2.5 designations signify the size of that particulate matter. The EPA defines PM2.5 levels as inhalable particles with diameters of 2.5 micrometers or smaller. These particles come in many sizes and shapes and can be made up of hundreds of different chemicals. Some are emitted directly from a source, such as construction sites, unpaved roads, fields, smokestacks or fires. Most particles form in the atmosphere as a result of complex reactions of chemicals such as sulfur dioxide and nitrogen oxides, which are pollutants emitted from power plants, industries and automobiles.

Our project was to basically predict the levels of PM 2.5 matter in the air based on the following parameters

1. temperature
2. pressure
3. rain
4. wind direction
5. wind speed

of every hour of the days in the years from 2013 to 2017.

This Repo contains an Pm2.5 Jupyter notebook file which contains code for Feature Engineering , Eda , Machine Learning Model Building and finally Cross Validation and Hyper Parameter Tuning . Check out here

This Repo contains an app.py where we made a website from scratch using Streamlit library where also we deployed our Ml models Check out here

You can access the deployed project by clicking here .

Contributers - Prajna Jeet Ojha , Abhipsha Dash , Sai Simran Patro , Rohan Kumar Mohanty (Team - The Nova )