• B.Subba Reddy
• V.Poojitha
• Sk.Annu
• S.Lalinya
• Y.Karthik

The stock market is a subject of great interest to experts and the general public. Many laymen consider stock investing a risky activity, like trying to catch a falling knife, because they believe that price movements cannot be predicted. Experts say that the stock market works based on factors such as profits, losses, opening and closing prices, and the number of shares traded. They also show that stock markets show cyclical cycles, with periods of growth (bull market) and periods of decline (bear market). If people can predict these changes using today's technology, they will make a lot of money or lose it all. This is why there is an increasing interest in using machine learning to predict stock prices. By using machine learning algorithms to analyze historical data, we hope to more accurately predict future stock prices. This can help us make better investment decisions and increase profits while reducing risk. Machine learning models can analyze various factors that influence stock prices, such as company earnings, market trends, and economic indicators. By understanding these trends, we can better predict when stock prices will rise or fall. Data quality, interpretation of results, and legal issues are important considerations. However, with careful observation and continuous improvement, machine learning can change the way we invest in the stock market.

ENHANCED STOCK MARKET FORECASTING THROUGH MACHINE LEARNING TECHNIQUES

"ENHANCED STOCK MARKET FORECASTING THROUGH MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.d897-d901, May-2024, Available :http://www.jetir.org/papers/JETIR2405400.pdf

"ENHANCED STOCK MARKET FORECASTING THROUGH MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppd897-d901, May-2024, Available at : http://www.jetir.org/papers/JETIR2405400.pdf