Stock Market Price Prediction Using Deep Learning

The stock market's volatility which makes it challenging for human traders to make accurate price predictions has led to a notable increase in prediction models over the past few decades. This study uses deep learning (DL) and machine learning (ML) approaches to predict stock values in the Indian stock market. The primary goal is to help intraday and short-term traders make informed decisions by offering data-driven forecasts. This study included three powerful algorithms: Long Short-Term Memory (LSTM), XGBoost and Random Forest (RF). To improve prediction accuracy an ensemble learning technique lowers overfitting and boosts generalization RF. XGBoost which is renowned for its effectiveness and superior performance uses gradient-boosting techniques to maximize predictions. The goal of the study is to determine the most effective model for forecasting stock market trends by evaluating and contrasting the accuracy of these three algorithms. The study takes into account a number of variables in order to create a reliable prediction model such as past prices trade volume and market patterns. The results of this initiative will be especially helpful to novice investors allowing them to make less risky data-driven investment choices. By offering more accurate forecasts the findings may potentially help to enhance algorithmic trading techniques.