  We explore the efficacy of machine learning (ML) in characterizing exoplanets
into different classes. The source of the data used in this work is University
of Puerto Rico's Planetary Habitability Laboratory's Exoplanets Catalog
(PHL-EC). We perform a detailed analysis of the structure of the data and
propose methods that can be used to effectively categorize new exoplanet
samples. Our contributions are two fold. We elaborate on the results obtained
by using ML algorithms by stating the accuracy of each method used and propose
the best paradigm to automate the task of exoplanet classification. The
exploration led to the development of new methods fundamental and relevant to
the context of the problem and beyond. Data exploration and experimentation
methods also result in the development of a general data methodology and a set
of best practices which can be used for exploratory data analysis experiments.
