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Astroinformatics is a new impact area in the world of astronomy, occasionallycalled the final frontier, where several astrophysicists, statisticians andcomputer scientists work together to tackle various data intensive astronomicalproblems. Exponential growth in the data volume and increased complexity of thedata augments difficult questions to the existing challenges. Classicalproblems in Astronomy are compounded by accumulation of astronomical volume ofcomplex data, rendering the task of classification and interpretationincredibly laborious. The presence of noise in the data makes analysis andinterpretation even more arduous. Machine learning algorithms and data analytictechniques provide the right platform for the challenges posed by theseproblems. A diverse range of open problem like star-galaxy separation,detection and classification of exoplanets, classification of supernovae isdiscussed. The focus of the paper is the applicability and efficacy of variousmachine learning algorithms like K Nearest Neighbor (KNN), random forest (RF),decision tree (DT), Support Vector Machine (SVM), Na\"ive Bayes and LinearDiscriminant Analysis (LDA) in analysis and inference of the decision theoreticproblems in Astronomy. The machine learning algorithms, integrated intoASTROMLSKIT, a toolkit developed in the course of the work, have been used toanalyze HabCat data and supernovae data. Accuracy has been found to beappreciably good.

ASTROMLSKIT: A New Statistical Machine Learning Toolkit: A Platform for Data Analytics in Astronomy