Application of Machine Learning Algorithm in Indian Stock Market Data
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 3
Abstract
Abstract: Prediction of Indian stock market data with data mining technique is one of the fascinating issues for researchers over the past decade. Statistical and traditional methods are no longer feasible for proper analysis of huge amount of data. With the help of Data mining technique information technology tool , it is able to uncover hidden patterns and predict future trends and behavior in stock market. In this paper there are combination of four supervised machine learning algorithms, classification and regression tree (CART), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are proposed for classification of Indian stock market data. These resulted forms help market analyst to make decision on selling, purchasing or holding stock for a particular company in Indian stock market. In section IV and V, experimental results and performance comparison section show that classification and regression tree misclassification rate is only 56.11% whereas LDA and QDA show 74.26% and 76.57% respectively. Smaller misclassification reveals that CART algorithm performs better classification of Indian stock market data as compared to LDA and QDA algorithms.
Authors and Affiliations
Sanjaya kumar Sen , Dr. Subhendu kumar Pani
Keywords
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- EP ID EP162832
- DOI 10.9790/0661-16320610
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