Identification of outliers in pollution concentration levels using anomaly detection

Anomaly detection is generally an identification of any odd or anomalous data sometimes even called as an outlier from a give pattern of data. It involves machine learning technique to learn the data and determine the outliers based on a probability condition. Machine learning, a branch of artificial intelligence plays a vital role in analyzing the data and identifies the outliers with a good probability. The objective of this paper is to determine the outlier of pollutant's concentration based on anomaly detection techniques and describe the air quality standards of the particular area.