Amazing-Python-Scripts

User Guide: Air Pollution Prediction

This user guide provides step-by-step instructions on how to use the Air Pollution Prediction script in the Amazing-Python-Scripts repository. This script uses data from the World Air Quality Index API to predict air pollution levels in a given city.

Step 1: Install Dependencies Before you can use the Air Pollution Prediction script, you need to make sure that you have all the necessary dependencies installed. This script requires Python 3 and the requests and matplotlib libraries. To install these dependencies, open a terminal or command prompt and run the following command:

pip install requests matplotlib

Step 2: Obtain an API Key To use the World Air Quality Index API, you will need to obtain an API key. You can do this by visiting the following website and following the instructions to create an account and obtain an API key: https://aqicn.org/data-platform/token/

Step 3: Download the Script Next, you need to download the Air Pollution Prediction script from the Amazing-Python-Scripts repository. You can do this by visiting the repository on GitHub and navigating to the Air pollution prediction directory. From there, you can download the CodeAP.py file to your computer.

Step 4: Run the Script Once you have downloaded the script and obtained an API key, you can run it by opening a terminal or command prompt, navigating to the directory where you saved the file, and running the following command:

python CodeAP.py

This will start the script and prompt you to enter your city name and API key.

Step 5: View the Results After entering your city name and API key, the script will retrieve air pollution data from the World Air Quality Index API and display it in a pie chart. The chart shows the relative amounts of different pollutants in the air, such as PM2.5, PM10, NO2, SO2, and O3.

You can use this information to get an idea of air pollution levels in your city and take appropriate precautions.

You can experiment with different cities to see how their air pollution levels compare.