Using an Artificial Neural Network for Air Quality Prediction

Air Quality Index is based on the measurement of particulate matter, Ozone, Nitrogen Dioxide, Sulfur Dioxide, and Carbon Monoxide emissions. Most of the stations on the map are monitoring both PM2.5 and PM10 data, but there are few exceptions where only PM10 is available, Here we are using the Bangalore weather data, and some of the features might even make the predictions worse. An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another, here it would help us how we can make neurons on-air live air data, try to find the best mean squared error. Input Layer - This is the first layer in the neural network.