Stock Price Prediction Using Machine Learning

Authors- Aseem Farajallah. B, Professor Dr.Prasanna.S

Abstract-This project explores the application of machine learning techniques for predicting stock prices, a key challenge in the financial industry. By analyzing historical stock data, including price trends, trading volumes, and other relevant market indicators, the study aims to forecast future stock prices with high accuracy. Various machine learning models, including regression analysis, support vector machines (SVMs), and deep learning methods, are employed to capture complex patterns in the data. The goal is to provide traders and investors with a predictive tool to enhance decision-making and optimize financial strategies. The results of this project highlight the potential of machine learning in transforming stock market predictions and improving investment outcomes. Furthermore, the project investigates feature selection techniques to identify the most impactful variables for prediction, improving model efficiency. Through extensive testing and model evaluation, it demonstrates how machine learning can adapt to the dynamic nature of financial markets. Ultimately, this approach can potentially automate and optimize trading strategies for better returns.

DOI: /10.61463/ijset.vol.13.issue3.144