Stock Price Prediction App using LSTM and Machine Learning.

This repository contains a stock price prediction application built using LSTM (Long Short-Term Memory) and other machine learning techniques. The app aims to forecast future stock prices based on historical data and provides users with valuable insights for making informed investment decisions.

Table of Contents.

• Introduction
• Features
• Requirements
• Installation
• Usage
• Model Training
• Results
• Contributing
• License

Introduction.

Stock price prediction is a challenging problem, and this application utilizes LSTM, a type of recurrent neural network (RNN), to capture long-term dependencies in historical stock data. The app preprocesses the data, trains the LSTM model, and generates predictions for future stock prices.

Features.

• User-Friendly Interface: The app provides an intuitive interface for users to enter stock symbols and select prediction timeframes easily.

• Real-Time Data: The application fetches real-time stock price data from reliable financial APIs, ensuring the most up-to-date predictions.

• Predictive Analytics: Utilizing LSTM and other machine learning techniques, the app offers accurate stock price predictions for the chosen timeframes.

• Visualization: The app displays visually appealing charts and graphs to illustrate historical and predicted stock price trends.

Requirements

• Python 3.x
• Flask
• Pandas
• NumPy
• TensorFlow
• Keras
• Matplotlib

Installation

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/stock-prediction-app.git
   cd stock-prediction-app
   

2. Install the required dependencies using pip:

   pip install -r requirements.txt
   

Usage

1. Run the Flask app:

   python app.py
   

2. Open your web browser and navigate to http://localhost:5000 to access the application.

Model Training

To retrain the LSTM model or experiment with different parameters:

1. Modify the model architecture and hyperparameters in model.py.

2. Prepare your historical stock price data in a CSV format with columns: 'Date' and 'Close'.

3. Run the training script:

   python train_model.py --data your_data.csv
   

Results

[Include any notable results, accuracy metrics, and sample predictions here.]

Contributing

We welcome contributions from the community! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.

License

[Add your license information here, for example: This project is licensed under the MIT License - see the LICENSE file for details.]

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[Add any additional information or acknowledgments here.]

Happy stock prediction! 🚀📈