Machine Learning-based Stock Market Price Prediction

Prediction of stock market price is a difficult and complex endeavour since stock prices are very unpredictable and change regularly. To make educated judgments, investors and businesses require precise forecasts. However, it is still quite challenging to use extracted criteria to forecast future stock values with high accuracy. Traditional strategies sometimes fail to appropriately meet the market's changing nature. In this research, we offer a system that uses machine learning methods like ARIMA, Linear Regression and LSTM networks to enhance accuracy in stock prices prediction. By combining these techniques, our strategy seeks to meet the needs of a constantly changing commercial and industrial environment while improving the accuracy and reliability of stock market predictions.