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ISSN 2063-5346
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IMPROVED ACCURACY IN PREDICTION OF STOCK EQUITY ANALYSIS AND CLASSIFICATION USING LINEAR REGRESSION AND COMPARED WITH DECISION TREE

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D. Bharath Kumar, S. Magesh Kumar
» doi: 10.31838/ecb/2023.12.sa1.460

Abstract

Aim: The main aim of the research work is to predict the stock equity using the Linear Regression(LR) over the Decision Tree(DTA). Materials and Methods: The two algorithms linear regression and decision tree are compared with a sample size = 10. Sample size is calculated using G power software and determined as 10 per group with pretest power 80%, threshold 0.05% and CI 95%. Results: The analysis of the results shows that the Linear Regression has a high accuracy of (94.38%) in comparison with the Decision Tree (89.47). Attained Significance Accuracy value is 0.894 (p>0.05). There is a statistically insignificant difference between the study groups with these algorithms. Conclusion: Prediction in classifying from the results it is concluded that the proposed algorithm Linear Regression will produce better results than the Decision tree algorithm.

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