| Abstrakt: |
Improving the accuracy and precision of stock market share price predictions using Innovative Long Short-Term Memory compared to Linear Regression (LR) is the major purpose of this research study. This paper's dataset demonstrates the approach's efficacy using the publicly accessible dataset from the National Stock Exchange (NSE). For this stock market prediction, we used a sample size of 280 (140 in Group 1 and 140 in Group 2), and we used G-power 0.8 with alpha and beta values of 0.05 and 0.2, respectively, and a confidence interval of 95%. With a number of samples (N=10), LR achieves better accuracy and precision in predicting stock prices on the stock market. The LR classifier's accuracy rate is 86.63%, while the Novel Long Short-Term Memory classifier's rate is 93.94%. A relevance score of p<0.05, or p=0.0271, indicates that the research is valid. In conclusion, when it comes to predicting stock prices on the stock market, Novel Long Short-Term Memory outperforms LR in terms of accuracy and precision. [ABSTRACT FROM AUTHOR] |
