Paper Contents
Abstract
A company's initial public offering (IPO), when it first sells shares on the market, is what initially determines stock prices. To calculate the appropriate price for the stock, investment firms consider a number of factors, including the total number of shares being offered. After that, the aforementioned factors will cause the share price to fluctuate, mostly based on the earnings that the company is anticipated to produce. Traders continuously assess a company's value using financial metrics, such as its profits history, market fluctuations, and the profit that may be reasonably anticipated. However, any attempt at prediction would be quite impossible due to the stock market's intricacy and turbulence. As a result, stock price forecasting has grown in significance as a field of study. The goal is to forecast machine learning-based stock price prediction methods. SMLT's supervised machine learning technique (SMLT) analyzes the dataset utilizing univariate, bivariate, and multivariate analysis. to suggest a machine learning-based technique for precisely forecasting stock price. The best accuracy with precision, recall, and F1 Score can be compared to the proposed machine learning algorithm technique.
Copyright
Copyright © 2023 Kusuma Polanki. This is an open access article distributed under the Creative Commons Attribution License.
