/Stock-predictor

Primary LanguagePython

Predicting Stock Prices with LSTM and Flask

This is a project that aims to predict stock prices using Long Short-Term Memory (LSTM) models and Flask framework.

Requirements

To run this project, you need to have the following requirements installed:

Python 3 Docker To install Python 3, you can follow the instructions provided on the official website. To install Docker, you can follow the instructions provided on the official website.

Installation

To install this project, you can follow these steps:

  • Clone this repository to your local machine using the following command: git clone https://github.com/your-username/predicting-stock-prices-with-lstm-and-flask.git Replace your-username with your actual username on GitHub.

  • Navigate to the project directory: cd predicting-stock-prices-with-lstm-and-flask

  • Create a virtual environment for the project: python -m venv venv

  • Activate the virtual environment: venv\Scripts\activate # Windows source venv/bin/activate # Unix/Linux

Install the required packages: pip install -r requirements.txt

Usage

To use this project, you can follow these steps:

Start the Flask app: flask run

  1. Open your web browser and go to http://localhost:5000/ to access the index page.
  2. Enter the stock symbol (e.g., AAPL for Apple Inc.) and the number of days to predict, then click the "Predict" button.
  3. Wait for the prediction to be displayed on the web page.
  4. To view the dashboard, go to http://localhost:5000/dashboard on your web browser.

The dashboard will display a line chart that shows the actual and predicted stock prices over time, as well as the accuracy of the model.

Docker

This project also includes a Dockerfile that you can use to build a Docker image for the project. To do so, you can follow these steps:

Build the Docker image: docker build -t predicting-stock-prices .

Run the Docker container: docker run -p 5000:5000 predicting-stock-prices

Access the Flask app in your web browser at http://localhost:5000/ as usual.

Jenkins

Adding a Jenkins Webhook

  1. Install the Jenkins Git plugin.
  2. Create a new Jenkins job:
  3. Set up a webhook on GitHub:
    • Navigate to your repository on GitHub and click on "Settings" in the top right corner of the screen.
    • Click on "Webhooks" in the left sidebar, then click "Add webhook".
    • In the "Payload URL" field, enter the URL for your Jenkins server followed by /github-webhook/, e.g. http://localhost:8080/github-webhook/.
    • Set the "Content type" to "application/json".
    • Under "Which events would you like to trigger this webhook?", select "Just the push event".
    • Click "Add webhook" to save your changes.

CI/CD pipeline

  1. Setup Jenkins
  2. Setup webhook
  3. Test pipeline Now that everything is set up, you can test your CI/CD pipeline by making a change to your code and pushing it to your Git repository.

Jenkins will automatically detect the change and trigger a build. Once the build is complete, your Docker image will be pushed to Docker Hub and your application will be updated with the new changes.

Credits

This project was inspired by the following resources:

Predicting Stock Prices with LSTM by Pablo Castilla How to Build a Python Flask Web App Serverless with AWS Lambda, API Gateway, & DynamoDB by Tech with Tim