Stock Market Prediction using Machine Learning

Abstract

Stock market prediction has been a subject of significant interest and research for both financial analysts and machine learning practitioners. This abstract presents a concise overview of the key aspects and approaches in the realm of stock market prediction. The unpredictable and dynamic nature of financial markets poses a challenge for accurate forecasting. However, advancements in machine learning techniques, availability of large-scale financial data, and computational power have led computational to the development of sophisticated prediction models. In this endeavour, we investigate the application of various machine learning algorithms, including regression, time series models and support vector machine, to forecast stock prices. The research focuses on data preprocessing, feature engineering, and model evaluation to enhance prediction accuracy. Using a diverse dataset Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are utilized to measure model performance. While acknowledging the inherent uncertainty of financial markets, this research contributes to the broader dialogue on data-driven decision-making in investment and finance. The outcomes of this study offer insights into the strengths and limitations of machine learning techniques in stock price prediction.