Stock Market Prediction Web App Using LSTMModel and Facebook Prophet

The Prophet and LSTM (Long Short-Term Memory) models combined powers are presented in the project as comprehensive method of stock price prediction through a single Python script. The script preprocesses historical stock data for both models using information received from yfinance. Time series forecasting is done with the Prophet model, which projects stock prices for the next 365 days and uses Plotly to visualise the results. The script divides the information into sets for testing and training, scales the information, then trains an LSTM model on the training set simultaneously. For a certain time period, the closing prices are projected using the LSTM model, and the forecasts are assessed using RMSE, or root mean squared error. Using Plotly, The script further illustrates the difference between real and forecasted stock values. This research offers an integrated approach to forecast stock prices by combining Sequential data processing's capabilities of the LSTM model with the forecasting skills of Prophet. It draws attention to how well these models complement one another in explaining various facets of stock price movements and emphasises how important it is to use machine learning predictions into a more comprehensive approach to investing analysis. To sum up, this study demonstrates how Prophet and LSTM models may be used in concert to predict stock prices, offering important new perspectives on the potential use of these models in financial forecasting. It emphasises how important it is to make well- informed decisions and seek professional advice when using machine learning models for investing.