Project on Stock Market

The stock market is a vital component of the global economy, influencing investment decisions, wealth creation, and financial planning. Due to its volatile nature and dependency on various economic, political, and social factors, predicting stock prices has always been a complex and challenging task. Traditionally, financial analysts rely on historical patterns, technical indicators, and economic reports to make investment decisions. However, these approaches often fall short in capturing the dynamic, nonlinear behavior of stock prices, especially in real-time environments.With advancements in Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful tools for financial forecasting. This study aims to predict stock market prices using a combination of ML models— such as Linear Regression, Random Forest, and Support Vector Machines (SVM)— and DL models, specifically Long Short-Term Memory (LSTM) networks. The models were trained on historical stock data, and their performances were evaluated using standard metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score. The study also incorporates technical indicators like moving averages and trading volumes to enhance predictive capability.The results demonstrate that while ML models perform well in identifying linear trends and shortterm patterns, LSTM models are significantly better at capturing long-term dependencies and sequential patterns in time-series data. The deep learning model provided more stable and accurate predictions, especially in volatile market conditions. This research highlights the potential of integrating ML and DL techniques into stock trading strategies, offering valuable decision-making support for investors, analysts, and financial institutions