This project aims to predict the PM 2.5 Air Pollution using the suitable weather data which is readily available. To solve this problem, firstly, exploratory data analysis will be conducted on available weather and pollution datasets to discover the correlation between different features. After applying suitable data cleaning and pre-processing methods, the machine learning techniques such as classification and regression models will be applied.
PM stands for Particulate Matter (also called particle pollution): the term for a mixture of solid particles and liquid droplets found in the air. Some particles, such as dust, dirt, soot, or smoke, are large or dark enough to be seen with the naked eye. Others are so small they can only be detected using an electron microscope. Particle pollution includes: • PM10: inhalable particles, with diameters that are generally 10 micrometres and smaller; and • PM2.5: fine inhalable particles, with diameters that are generally 2.5 micrometres and smaller. How small is 2.5 micrometres? Think about a single hair from your head. The average human hair is about 70 micrometres in diameter – making it 30 times larger than the largest fine particle.
Particulate matter contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. Some particles less than 10 micrometres in diameter can get deep into your lungs and some may even get into your bloodstream. Of these, particles less than 2.5 micrometres in diameter, also known as fine particles or PM2.5, pose the greatest risk to health.
Fine particles are also the main cause of reduced visibility (haze) in parts of the United States, including many of our treasured national parks and wilderness areas.
• Import the necessary libraries and loading the data • Data preprocessing • Exploratory Data Analysis • Model training and Evaluation • Saving the model