LSTM-Based-Stock-Price-Forecasting-System

Built an end-to-end LSTM-based time series forecasting system to predict stock closing prices using historical market data, with preprocessing, model evaluation, and next-30-day forecasting.Deployed the model using Flask and Streamlit, enabling interactive visualization of historical trends and real-time forecasted prices.

Tools & Technologies: Python, LSTM (TensorFlow/Keras), Time Series Analysis, Pandas, NumPy, yFinance, Flask, Streamlit, Plotly, scikit-learn, Git/GitHub.