Stock Data Analysis and Prediction Using Machine Learning

Abstract

Our goal in this project is to use machine learning methods to forecast stock prices. In the field of predicting stock prices, machine learning has excelled. Our goal is to improve investing decision-making via precise stock price forecasting. We recommend the use of a stock price forecasting system that combines mathematical operations, machine learning algorithms, and outside factors in order to achieve this goal. By doing this, we hope to improve stock prediction accuracy and find lucrative trading opportunities. There are two types of intraday trading for stocks: "day trading," which involves holding assets for a single day, week, or even month, and "multi-day trading." In tasks requiring sequence prediction, Long Short-Term Memory (LSTM) models perform very well, especially when past knowledge is essential. In this situation, past stock prices are very important in predicting future prices. Although it is difficult to anticipate a stock's precise price with any degree of accuracy, we can create a model that predicts whether the price will rise or fall.