Stock Market Price Predictions Using Machine Learning

Abstract

The stock market is a dynamic and complex system influenced by numerous factors, making the accurate prediction of stock prices a challenging task. This project focuses on developing a web-based platform that predicts and highlights stocks expected to increase in price. The primary goal is to assist investors by simplifying the decision-making process through real-time insights into market trends. To achieve this, we employ machine learning algorithms trained on historical stock market data, including features such as opening and closing prices, trading volume, market sentiment, and technical indicators. These models analyze patterns and trends to forecast short-term price movements. Rather than displaying all market data, the system filters and showcases only those stocks that are predicted to experience an upward trend, helping users to quickly identify potential investment opportunities. The platform is designed with a clean, responsive web interface where users can view the list of increasing stocks in real time. The backend continuously fetches and updates financial data from reliable sources, processes it through the trained prediction models, and displays the results on the website. This automation ensures that the information remains current and actionable. By narrowing the focus to only rising stocks and presenting them in an accessible format, the system offers a practical tool for both novice and experienced investors. It combines the power of data science with the convenience of a web application, aiming to enhance investment strategies and support smarter financial decisions.