Stock Price Prediction Using Machine Learning

One of the most difficult and well-researched issues in data science and financial engineering is stock market prediction. Economic conditions, political developments, investor emotion, and technical indications are just a few of the many variables that affect financial markets, making them extremely unpredictable and challenging to forecast. This research offers a thorough application of machine learning techniques for stock price prediction. Data gathering, pre-processing, and feature engineering using a variety of technical indicators are all part of the study. A variety of models are created and assessed, including deep learning architectures like Long Short-Term Memory (LSTM) networks and traditional machine learning techniques. Additionally, the model’s performance is evaluated under genuine trading conditions through the application of hyper parameter tuning and rigorous back testing strategies. Machine learning models are capable of identifying meaningful patterns in historical data and can generate profitable, risk-adjusted trading signals when applied meticulously, despite the fact that perfect prediction is not feasible. The results support this assertion.