monly98 / model_flask_deployment

This project demonstrates the deployment of machine learning models using Flask, a lightweight web framework for Python. The goal is to showcase a simple yet powerful way to turn your trained models into interactive web applications.

Model Deployment with Flask

This project demonstrates the deployment of machine learning models using Flask, a lightweight web framework for Python. The goal is to showcase a simple yet powerful way to turn your trained models into interactive web applications.

Overview

In this project, we guide you through the process of deploying a machine learning model (such as a classifier or regressor) as a web application using Flask. Flask provides a convenient and extensible way to expose your models to the world, allowing users to interact with them through a user-friendly interface.

Key Features

• Model Integration: Learn how to integrate your pre-trained machine learning model into a Flask application.

• API Endpoints: Create API endpoints to handle model predictions, enabling seamless communication between the front-end and back-end.

• User Interface: Design a simple yet effective user interface to capture input data and display model predictions.

• Scalability: Understand how Flask enables the deployment of machine learning models that can scale as user demands increase.

Getting Started

Follow these steps to get the project up and running on your local machine:

Prerequisites

Make sure you have Python and pip installed. If not, you can download them from python.org and pip.pypa.io, respectively.

Installation

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/model-deployment-with-flask.git

2. Navigate to the project directory:

   cd model-deployment-with-flask

3. Install the required dependencies:

   pip install -r requirements.txt

   This will install all the necessary Python packages listed in the requirements.txt file.

Usage

1. Run the Flask application:

   python app.py

2. Open your web browser and go to [http://127.0.0.1:5000/] to interact with the deployed machine learning model.

Contributing

We welcome contributions! Whether it's bug fixes, new features, or improvements to the documentation, your input is valuable. Refer to our Contribution Guidelines for details on how to get involved.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute the code according to the terms outlined in the license.

Acknowledgments

• Special thanks to the Flask and scikit-learn communities for their fantastic tools and resources that make model deployment accessible to everyone.