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Abstract
IT industries are currently facing a major challenge of mapping candidate’s skill set from the available pool to fill various job positions.As the data is increasing at enormous speed, the issue is escalating to next higher levels wherein expertise repositories are piled up with profile records (say CVs), but mining becomes a cumbersome task. In this paper, we made an attempt to sensitize stakeholders of domains of interest to get acquainted with the present State-Of-The-Art of automatic classification of resumes. Researchers classified resumes into various categories and traversed it end to end to extract the relevant features using text mining, Natural Language Processing, crawlers, Deep learning, glove-word embedding, convolution neural network, K-Means Clustering, support vector machine and decision tree classifier etc. Different Machine Learning Algorithms are applied on various Resume Analysis Frameworks and compared on the basis of various metrics like Root Mean Square Error (RMSE), accuracy, recall, relative absolute error and precision etc.