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The repository contains a sample application that demonstrates how to use Flask to build a web service that exposes an ML model as an API endpoint. It includes a detailed step-by-step guide on how to set up and deploy the Flask application.

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Flask-based Deployment of Bank Note Authenticator

Welcome to the Flask-based deployment of a machine learning model that can authenticate bank notes. This project is part of my journey in learning and putting my skills to use.

Project Overview

The goal of this project is to deploy a machine learning model that can authenticate bank notes. The model is trained on a dataset from Kaggle, containing samples of images of bank notes, along with labels indicating whether each note is real (1) or fake (0).

In this repository, you will find a Flask-based implementation of the model.

Getting Started

To get started with this project, you will need to:

  1. Clone this repository to your local machine
  2. Install the required dependencies using pip
  3. Download the dataset from Kaggle and preprocess it using the provided Jupyter notebook
  4. Train the machine learning model using the preprocessed data
  5. Deploy the model using Flask

Contributing

This project is a work in progress, and I welcome contributions from the community. If you have any suggestions, feedback, or bug reports, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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The repository contains a sample application that demonstrates how to use Flask to build a web service that exposes an ML model as an API endpoint. It includes a detailed step-by-step guide on how to set up and deploy the Flask application.

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