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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference =======

SJIM2DSG5T4

APPLE Stock Price Prediction

Project Overview

This project involves the development of a financial application to predict the stock price of APPLE using deep learning techniques. The application utilizes an optimized LSTM model trained on data scraped from Yahoo Finance. The web application will be deployed on a cloud server for online consultation and will be accessible on both PCs and mobile devices.

Authors

  • —DJEUMEZA JULIE
  • —YANNICK SIMO

Objectives

  • —Scrape APPLE stock data from Yahoo Finance.
  • —Develop and optimize an LSTM model to predict APPLE stock prices.
  • —Create a web application to predict the stock price for the next 5 days.
  • —Deploy the web application on a cloud server, making it accessible on all devices.

Getting Started

Prerequisites

  • —Python 3.x
  • —pip
  • —Virtual environment (optional but recommended)

Installation

  1. 1.Clone the repository:
sh
    git clone https://gitlab.com/academic-project616/sji_m2_ds_g5_t4.git
    cd sji_m2_ds_g5_t4
  1. 1.Create and activate a virtual environment (optional):
sh
    python -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`
  1. 1.Install the required packages:
sh
    pip install -r requirements.txt

Usage

  1. 1.Scrape the data from Yahoo Finance:
sh
    python scrape_data.py
  1. 1.Train the LSTM model:
sh
    python train_model.py
  1. 1.Run the web application locally:
sh
    python app.py
  1. 1.Access the application in your web browser at http://localhost:5000.

Deployment

To deploy the application on a cloud server, follow the instructions provided by your cloud service provider. Ensure that the application is accessible on both PCs and mobile devices.

Project Structure

  • —scrape_data.py: Script to scrape APPLE stock data from Yahoo Finance.
  • —train_model.py: Script to train the LSTM model.
  • —app.py: Main application script to run the web application.
  • —templates/: Directory containing HTML templates for the web application.
  • —static/: Directory containing static files (CSS, JavaScript) for the web application.
  • —requirements.txt: List of required Python packages.

License

This project is licensed under the MIT License.

Acknowledgments

We would like to thank our instructors and peers for their support and guidance throughout this project.

Project Status

The project is currently under active development. Future updates and improvements will be documented in this README.