Abstract: Stock prices are first determined by a company’s Initial Public Offering (IPO) when it first puts its shares into the market. Investment firms use a variety of metrics, along with the total number of shares being offered, to determine what the stock’s price should be. Afterward, the several reasons mentioned above will cause the share price to rise and fall, driven largely by the earnings that can be expected from the company. Traders use financial metrics constantly to determine the value of the company, including its history of earnings, changes in the market, and the profit that it can reasonably be expected to bring in. Hence, stock price prediction has become an important research area. The aim is to predict machine learning based techniques for stock price prediction. The analysis of dataset by supervised machine learning technique (SMLT) using uni-variate analysis, bi-variate and multi-variate analysis. To propose a machine learning-based method to accurately predict the stock price. Proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and F1 Score.

Keywords: Machine learning, Regression, tesla

Works Cited:

Vinodhini. D, Mr. Manikadan. N " PROJECTING THE PRICE OF STOCKS USING REGRESSION MODEL", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 42-48, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12905

PDF | DOI: 10.17148/IJARCCE.2023.12905

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