Regression Based Intelligent Mechanism For Prediction Of Stock Values In Real-Time Invision

Abstract

A company's stock price, which might increase in tandem with the price of a single share, is the most useful indicator of its success. Businesses therefore try to persuade their clients to purchase their stocks by advertising them to them. Clients or stockholding firms find it challenging to predict the future value of a single stock due to price volatility. As a result, stock market forecasting has become the most popular topic in the corporate sector. As a result, it is crucial to solve this problem for the benefit of buyers and investors because they frequently experience investment losses, which can be resolved by a variety of machine learning algorithms. One of the best machine learning statistical methods for predictive analysis, linear regression, and Python are being used to create a stock price prediction website to address this issue. The prediction is based on past data. Finding a way to employ linear regression models to get more accurate values is the main objective. The dataset that will be used to train the linear regression models can be altered to obtain more accurate results. To forecast stock market analysis, this research aims to show that linear regression is the most suitable and efficient technique.