Stock Market Prediction using different Machine Learning Algorithms

Stock market prediction is a difficult yet crucial task that has piqued the interest of both academics and investors. Due to their capacity to process and analyze huge chunk of data in real-time, algorithms based on machine learning have become an effective tool for predicting stock values. We explore the usage of different machine learning approaches for stock market prediction in this research, including decision trees, random forests, support vector machines, neural networks, and deep learning techniques. We cover the benefits and drawbacks of each algorithm, as well as its applicability to various sorts of data roots such as finance/business records, news articles, and social media. We additionally look at the difficulties that come with using machine learning algorithms for stock market prediction, such as overfitting, data quality, and comprehensibility. Lastly, we describe some recent research that have employed machine learning algorithms to anticipate equity markets, as well as assess their effectiveness and practical utility. Overall, this study gives a thorough review of the state of the art in stock market prediction using machine learning algorithms, identifying the significant obstacles and potential in this interesting research area.