Stock Price Prediction using Neural Networks

This project demonstrates the use of neural networks for predicting stock prices using historical data. It leverages machine learning techniques implemented with TensorFlow and Keras to build predictive models that can forecast future stock prices based on past performance.

Key Features

• Data Collection: Historical stock price data is obtained using the yfinance library, allowing access to a wide range of financial data for various stocks.
• Data Preprocessing: Data preprocessing tasks, including cleaning, normalization, and feature engineering, are performed using numpy and pandas to prepare the data for modeling.
• Model Development: Neural network models are built and trained using TensorFlow and Keras, utilizing various architectures such as feedforward networks, recurrent neural networks (RNNs), or long short-term memory networks (LSTMs).
• Model Evaluation: The performance of the trained models is evaluated using metrics such as mean squared error (MSE), mean absolute error (MAE), or accuracy, providing insights into the predictive capabilities of the models.
• Visualization: Matplotlib is used to visualize historical stock price data, model predictions, and evaluation metrics, enabling users to gain insights into the behavior of the stock prices and the effectiveness of the predictive models.
• Interactive Interface: The Streamlit library is employed to create an interactive web application where users can input stock symbols, select prediction horizons, and visualize predictions in real-time.

Libraries Used

• numpy: Efficient numerical operations for data preprocessing and manipulation.
• pandas: Data structures and tools for data analysis and manipulation.
• matplotlib: Plotting library for creating visualizations of historical data and model outputs.
• streamlit: Framework for building interactive web applications for data science projects.
• TensorFlow: Open-source machine learning framework for building and training neural networks.
• Keras: High-level neural networks API, built on top of TensorFlow, for easy model building and experimentation.
• yfinance: Library for downloading historical market data from Yahoo Finance for analysis and modeling.

Getting Started

1. Clone the repository to your local machine.
2. Run the Streamlit application using streamlit run app.py.
3. Access the interactive web application in your browser to explore stock price predictions.

Contribution

Contributions to this project are welcome! If you have any suggestions, feature requests, or bug reports, please open an issue or submit a pull request.