• Chirag Suthar
• Shayam Devendra Shah
• Nikhil K Pawanikar
• R. Srivaramangai

For investors and traders looking to make educated investment decisions, stock market forecasting has long been a difficult and crucial topic of research. The use of machine learning techniques for stock market prediction has attracted a lot of attention in recent years because of their capacity to analyze enormous volumes of data and reveal hidden patterns. More than 50 research publications on machine learning algorithms for stock market prediction are examined in this paper. The reviewed papers are primarily used to analyze and forecast the direction and amount of changes in stock prices by looking at numerous data sources, such as financial reports, social media, and news articles. In order to determine the best method for stock market prediction, the study analyses various data sets using various prediction methodologies, including Bayesian model, Fuzzy classifier, Artificial Neural Networks (ANN), Support Vector Machine (SVM) classifier, Neural Network (NN), and other machine learning methods. The findings indicate that using alternate data sources can improve prediction accuracy and that machine learning techniques are useful for forecasting stock market changes. The ability of several machine learning techniques, including decision trees, neural networks, and support vector machines to forecast stock values, is also explored in the article, along with their advantages and disadvantages. The study sheds light on the potential advantages of utilizing machine learning methods and different data sources to improve stock market prediction accuracy. The findings imply that machine learning approaches can aid traders and investors in risk management, portfolio return optimization, and the identification of lucrative investment possibilities.

Stock market, Prediction techniques, Neural Networks, Long Short-Term Memory, Artificial Neural Network, Autoregressive Integrated Moving Average, Convolutional Neural Network, Re-current Neural Network, Random Forest, Sentiment Analysis

"Review of Prediction Techniques for Stock Market Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.c428-c437, July-2023, Available :http://www.jetir.org/papers/JETIR2307250.pdf

"Review of Prediction Techniques for Stock Market Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppc428-c437, July-2023, Available at : http://www.jetir.org/papers/JETIR2307250.pdf