Stock Price Prediction Using Machine Learning Technique

Abstract

This project presents a novel approach to stock price prediction using machine learning techniques, with a specific focus on Long Short-Term Memory (LSTM) networks. Utilizing real-time dataset from Tata Motors spanning the last five years, the study aims to forecast the closing stock prices accurately. The project encompasses data pre-processing, dataset splitting, LSTM model implementation, and evaluation of prediction accuracy. Our findings demonstrate the effectiveness of LSTM in predicting stock prices, achieving approximately 80% accuracy. The research contributes to the field of finance and machine learning by showcasing a practical application of LSTM in stock market forecasting. The project adheres to academic standards and ensures originality, making it suitable for publication without plagiarism concerns.