Yogendra630/stock-price-prediction-ml — reverse-engineered prompt

Build me a simple Python app that predicts stock prices using historical market data and an LSTM model. I want to enter or configure a stock ticker, have the app download the data automatically, clean it, use closing prices, normalize the values, create 60 day time sequences, train the model, and save it so I can reuse it later.

Please include a basic Streamlit dashboard where I can choose the stock, run a prediction, and see a chart comparing the real prices and predicted prices. Keep it beginner friendly and make sure it can also run from the command line with python main.py. Use TensorFlow or Keras, pandas, numpy, scikit learn, yfinance, matplotlib, and Streamlit. Save the trained model in a saved_model folder and keep the code organized so training, preprocessing, prediction, and visualization are easy to understand.

If anything has changed in the libraries, look up the current docs online and make it work end to end.