Review On Stock Market Price Prediction Using Machine Learning

Abstract: Stock market prediction has become a significant area of research due to its dynamic and highly volatile nature. Accurate forecasting of stock prices can assist investors in making informed financial decisions and minimizing risks. This paper presents a machine learning-based stock market prediction system that analyzes historical stock data to forecast future price movements. The proposed system utilizes advanced algorithms such as Long Short-Term Memory (LSTM) networks, which are well-suited for time series analysis, to capture complex patterns and trends in stock price data.

Stock market prediction is an important area of research in finance and machine learning. Predicting future stock prices helps investors make better investment decisions and reduce financial risks. However, stock prices are highly dynamic and influenced by various factors such as market trends, company performance, and economic conditions. This research proposes a stock market prediction system using Linear Regression and Long Short-Term Memory (LSTM) algorithms. Historical stock market data is collected and preprocessed before being used for model training. Linear Regression is used to identify linear relationships between stock features, while LSTM is employed to capture long- term dependencies and complex patterns in time-series data. The performance of both models is evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). Experimental results show that the LSTM model provides more accurate predictions compared to Linear Regression due to its ability to learn temporal patterns. The proposed system demonstrates the effectiveness of machine learning and deep learning techniques in stock market forecasting.

Keywords: Stock Market Prediction, Machine Learning, Deep Learning, Linear Regression, LSTM, Time Series Forecasting.