Our project focuses on the machine learning domain of AI. Techniques involving machine learning, primarily unsupervised learning will be used in this project. Unsupervised learning will be implemented through clustering by the technique of self organizing maps. The data provided will contain information regarding the emission of greenhouse gases in various regions throughout different years. From that data, we will predict the impact on temperature and other climatic changes in these regions. Implementation of AI in our project will be the clustering of data through self organizing maps on the climatic change predictions that we obtained initially. Self organizing maps are a type of Artificial Neural Network (ANN), which uses dimensionality reduction to represent its distribution as a map. Hence, it forms a map where similar samples are mapped closely together. We will be clustering regions based on similar climatic conditions, and they will be mapped with similar colors. This mapping will then be visualized through a world map, where regions with similar climatic conditions will be colored with a similar color.
Additional Libraries: Numpy, Matplot, Pandas, Scipy