Predictive Modeling of Stock Price Trends Using Machine Learning and Deep Learning Techniques-

:Predicting stock price movements has been challenging yet crucial for investors and financial ana- lysts. Fluctuations in stock prices are valuable economic indicators, providing insights into overall economic well- being, consumer confidence, and market sentiment. In this study, we evaluate the efficacy of three different ma- chine and deep learning algorithms in anticipating stock price trends. We assess the performance of Logistic Re- gression, Random Forest Regression, and Long Short- Term Memory (LSTM) algorithms in forecasting whether a stock's price will rise or fall in the upcoming period, utilising historical stock price data as input features. Our findings demonstrate that while each algorithm exhibits varying degrees of predictive accuracy, LSTM networks stand out as they generally outperform Logistic Regression and Random Forest Regression in capturing the complex tem- poral dependencies inherent in stock price data. This suggests that LSTM networks, with their superior perfor- mance, hold significant promise as effective tools for stock price trend prediction, particularly in volatile and non-lin- ear financial markets. This could be a game-changer in stock price prediction, instilling optimism about the fu- ture of stock market analysis..