An Analysis of the Supervised and Unsupervised Machine Learning in Enhancing the Efficacy of Financial Analysis
Himanshu Dahiya
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Abstract
Data mining is the process of discovering patterns, corresponding to valuable information from the large
data sets, involving methods at the intersection of machine learning, statistics, and database systems.
Evolving from the fields of pattern recognition and artificial intelligence, machine learning explores the
study and construction of algorithms that can learn from sample inputs. Financial data analysis is used
in many financial institutes for accurate analysis of consumer data to find defaulters, to reduce the
manual errors involved, for fast and saving time processing, to reduce the misjudgments, to classify the
customers directly, and to reduce the loss of the financial institutions. We have analyzed a lot of machine
learning techniques for financial analysis, namely models of supervised classification (Artificial Neural
Networks, SupportVector Machine, Decision Trees), those of prediction (Cox survival model, CART
Decision Trees), and also models of clustering(K-means clustering).
Keywords: Supervised and Unsupervised Machine Learning; data mining; Financial Analysis