Stock Price Prediction Website Using Linear Regression - A Machine Learning Algorithm

Abstract

The most valuable indicator of a company’s success is its stock price, which can rise in tandem with the price of a single share. For that reason, businesses advertise their stocks to their customers in an effort to get them to buy them. The volatility of stock prices makes it difficult for clients or stockholding companies to forecast the future value of a single stock. Therefore, stock market forecasting has emerged as the most well-liked topic in the corporate sector, and hence solving this problem has become so important for the betterment of the investors and buyers as many a times they have to face loss in their investment and this problem can be solve by various Machine learning algorithms. To solve this problem we are developing one stock price prediction website using Python and Linear Regression which is one of the best Machine Learning statistical method for predictive analysis. We are using historical Data for the prediction. Finding a method to use linear regression models to obtain more precise values is the major goal. To acquire more precise results from the linear regression models, it is possible to change the dataset that will be used to train the models. The purpose of this paper is to demonstrate that linear regression is the most appropriate and effective method for forecasting stock market analysis.