Development of a stock prediction system using Deep Learning Model Approach

Stock market is enormous and tough to comprehend. Because of the market’s high volatility, it is regarded as being uncertain and unpredictable. Investing in a good stock at the wrong moment can be fatal, whereas investing in a good stock at the right time can provide rewards. Financial investors nowadays face this trading dilemma since they do not fully comprehend which stocks to buy or which stocks to sell in order to achieve the best results. An attempt was made in this research to build and implement a stock prediction system employing Long Short-Term Memory (LSTM). It was built with Python and Stream lit. The system was tested numerous times with a variety of real-world datasets, and it was shown to be extremely efficient and better than the existing ones in handling the highlighted problems by implementing a suitable trading strategy in the web-based application. The study achieve an accuracy of 85%. The researchers believe that this research work will go a long way towards alleviating the suffering of the stock marketers by providing a better alternative for market prediction.