"Stock Price Prediction Project"

"This project presents a machine learning-based approach for stock price prediction using historical financial data. The system leverages data obtained from financial APIs and applied preprocessing techniques such as normalization and time-series windowing to prepare the dataset for modeling. A deep learning model, specifically Long Short-Term Memory (LSTM), is utilized to capture temporal dependencies and trends in stock price movements.The application is developed using Python and integrates libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow for data processing and model training. A user-friendly web interface is built using Streamlit, enabling users to select a stock ticker, visualize historical price data, and observe predicted trends interactively. Plotly is used to generate dynamic and interactive visualizations for better data interpretation.The primary objective of this project is to demonstrate how machine learning techniques can be applied to financial time-series forecasting. While the model provides insights into potential future trends, it is intended for educational and research purposes rather than financial decision-making. This project highlights the integration of data science, deep learning, and web technologies to create an end-to-end predictive analytics system."