Paper Contents
Abstract
The value of a company's stock, which can increase along with the cost of an individual share, is the best measure of its success. Because of this, companies promote their stocks to their clients in an effort to persuade them to purchase them. It is challenging for customers or stockholding companies to predict the future value of a single stock due to the volatility of stock prices. As a result, stock market forecasting has become the corporate sector's most popular topic, making it crucial to find a solution for the benefit of investors and buyers who frequently suffer losses on their investments. A number of machine learning algorithms can help with this problem. Using Python and Linear Regression, one of the top Machine Learning statistical methods for predictive analysis, we are creating a stock price prediction website to address this issue. We base our projection on previous data. The main objective is to discover a technique for using linear regression models to derive more precise values. It is feasible to alter the dataset that will be used to train the linear regression models in order to obtain results that are more accurate. The aim of this research is to show that the best and most efficient technique for forecasting stock market analysis is linear regression.Keywords. Machine Learning, Linear Regression, Python, Django framework, Yahoo Finance.
Copyright
Copyright © 2023 Vedashree Vilas Bhat. This is an open access article distributed under the Creative Commons Attribution License.
