Heroku Flask - Pickling a Model

This repository serves as a practical example of deploying a machine learning model on Heroku using Flask and pickling. It demonstrates how to push files to a Heroku server and make predictions using a model loaded from a pickle file.

Purpose

The primary purpose of this repository is to showcase the steps involved in deploying a machine learning model as a web service on Heroku. It covers the following key aspects:

• Flask Web Application: The project includes a Flask-based web application that exposes an API for making predictions with a machine learning model.

• Model Serialization: We use the pickle library to serialize a trained machine learning model, making it ready for deployment.

• Heroku Deployment: The repository demonstrates how to deploy the Flask application and the pickled model to a Heroku server, making it accessible to the outside world.

Getting Started

To get started with this example project, follow these steps:

1. Clone the Repository: Use git clone to clone this repository to your local machine.

2. Setup Your Environment: Create a virtual environment and install the necessary dependencies specified in requirements.txt.

3. Train Your Model: If you have your own machine learning model, you can train it or use an existing one. Serialize the trained model using the pickle library.

4. Deploy to Heroku: Follow the provided instructions to deploy the Flask application and model to Heroku.

5. Access the API: Once deployed, you can access the API endpoint to make predictions using your machine learning model.

Repository Structure

The repository structure is organized as follows:

• app.py: The Flask web application code.
• model.pkl: The serialized machine learning model (you should replace this with your own model).
• requirements.txt: Lists the Python dependencies required for the project.
• Procfile: Specifies the command to run the Flask app on Heroku.
• README.md: This README file.

Further Information

For detailed instructions and additional information about deploying machine learning models on Heroku with Flask and pickling, please refer to the project's documentation and guides.

Feedback and Contributions

If you have any feedback, questions, or would like to contribute to this project, please feel free to create issues or pull requests in this repository.

Happy coding and deploying!