Stock market prediction using LSTM

Abstract

The stock market prediction patterns are seen as an important activity and it is more effective. Hence, stock prices will lead to lucrative profits from sound taking decisions. Because of the stagnant and noisy data, stock market-related forecasts are a major challenge for investors. Therefore, forecasting the stock market is a major challenge for investors to use their money to make more profit. This presents a stock market prediction based on Long Short-Term Memory (LSTM), designed to handle time series data and long-term dependencies. Historical stock price data for major technology companies, including Apple, Amazon, Google, and Microsoft, is collected from Yahoo Finance. The dataset contains key attributes such as open price, close price, high price, low price, and trading volume. These features are used to analyze stock behavior and predict future price movements. LSTM-based models can capture temporal relationships in stock market data more effectively than traditional statistical methods.