Stock Market Prediction Using Deep Learning

Abstract: The stock market represents a dynamic and complex system influenced by various factors, making accurate prediction of stock prices a challenging task. In this investigation, we utilize deep learning techniques to boost the precision of predictions in the stock market. Specifically, we employ Long Short-Term Memory (LSTM) networks to analyze historical stock data and capture intricate patterns that traditional methods may overlook. Our methodology encompasses meticulous data preprocessing, encompassing feature selection and normalization, ensuring the optimal functionality of the chosen deep learning model. The dataset utilized, derived from NSE, provides a robust foundation for training and testing our model. Evaluation metrics, gauge the model's performance