• Miss. Archana Mate
• Walunj Gayatri
• Bagwan Ramiza
• Pawar Pratiksha

Abstract: In this article, we try to apply a machine learning strategy to forecast stock prices. Stock price forecasting uses machine learning effectively. The objective of stock price forecasting is to help investors make more informed and precise financial decisions. To improve stock forecast accuracy and generate lucrative trades, we advise using a stock price prediction system that incorporates mathematical operations, machine learning, and other external aspects. The stock market is notoriously unpredictable, making its forecasting a difficult undertaking. Stock prices are influenced by a variety of important physical, psychological, intellectual, and other important aspects in addition to economic variables. In this study, the Auto Regressive Integrated Moving Average (ARIMA) Model is used to forecast stock prices. Using published stock data that has been obtained from Yahoo Finance, stock price prediction models have been created and tested. The outcomes of the experiment support the claim that the ARIMA Model may reasonably predict stock values for a brief period of time.

Machine Learning, Stock Market, Predictive Analysis, Financial Time Series Forecasting

"STOCK PRICE PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.o241-o244, May-2023, Available :http://www.jetir.org/papers/JETIR2305F29.pdf

"STOCK PRICE PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppo241-o244, May-2023, Available at : http://www.jetir.org/papers/JETIR2305F29.pdf