• Vcv.Srinivas
• Mrs. S.Sunanda

Stock market prediction has long been an area of interest for investors, analysts, and researchers. The ability to forecast the direction of stock prices accurately can provide significant advantages in investment decision-making. With the advancements in machine learning techniques, particularly in the realm of predictive analytics, there has been a surge in research focused on developing models for stock market prediction. This paper presents an overview of various machine learning algorithms and techniques utilized for stock market prediction. Furthermore, the paper discusses the preprocessing steps involved in preparing historical financial data for analysis, such as feature selection, normalization, and handling missing values. Several machine learning algorithms commonly employed in stock market prediction are examined, including linear regression, support vector machines, decision trees, random forests, and deep learning models like recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). The strengths and weaknesses of each approach are discussed, along with considerations for model evaluation and selection. Moreover, the paper delves into the importance of feature engineering and the incorporation of domain knowledge in designing effective predictive models. It also discusses the significance of data quality, size, and frequency in influencing the performance of machine learning algorithms for stock market prediction

STOCK MARKET PREDICTION USING MACHINE LEARNING

"STOCK MARKET PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.30-34, June-2024, Available :http://www.jetir.org/papers/JETIRGL06005.pdf

"STOCK MARKET PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp30-34, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06005.pdf