Finding similarities, aka as clustering, in air polution of multiple areas of India. The dataset comprises three types of air pollutant in India for specific cities. Techniques used: K-means clustering, Hierarchical Clustering, Affinity Propagation, Agglomerative Clustering, BIRCH Clustering, DBSCAN and Gaussian Mixture Model.
Clustering of air pollution
This project is based on a public dataset where air pollution in India listed concentrayion of three main pollutant in cities. Using feature engineering and machine learning techniques divided the dataset to three meaningful categories.
Finding similarities, aka as clustering, in air polution of multiple areas of India. The dataset comprises three types of air pollutant in India for specific cities. Techniques used: K-means clustering, Hierarchical Clustering, Affinity Propagation, Agglomerative Clustering, BIRCH Clustering, DBSCAN and Gaussian Mixture Model.