Hotel Reservation Prediction

Live Application

Try before I am out of AWS credits: Hotel Reservation Prediction

Overview

This project demonstrates an end-to-end MLOps pipeline for predicting hotel reservations. It includes data ingestion, preprocessing, model training, and deployment. The application is deployed on AWS using EKS and Load Balancer, and it features a Flask-based web interface for user interaction.

Features

• MLOps Pipeline: Automates machine learning workflows.
• Flask Web Application: User-friendly interface for predictions.
• AWS Deployment: Hosted on AWS using EKS and Load Balancer.
• Dockerized Application: Fully containerized for portability.
• CI/CD Pipeline: Seamless integration and deployment with Jenkins and GitHub Actions.
• LightGBM Model: Efficient and accurate predictions.

Technologies Used

• Python: Core programming language.
• Flask: Web framework for the application.
• Docker: Containerization for consistent environments.
• AWS: Cloud platform for deployment.
• Jenkins: CI/CD pipeline for automation.
• GitHub Actions: For continuous integration and deployment.
• LightGBM: Machine learning model for predictions.

Getting Started

Dataset Link (https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset)

Prerequisites

• Python 3.9+
• Docker
• AWS CLI
• kubectl
• Jenkins (optional for CI/CD)

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/hotel-reservation-prediction.git
   cd hotel-reservation-prediction

2. Install dependencies:

   pip install -r requirements.txt

3. Build the Docker image:

   docker build -t flask-app .

4. Run the Docker container:

   docker run -p 5000:5000 flask-app

5. Access the application: Open your browser and navigate to http://localhost:5000.

Deployment on AWS

1. Push the Docker image to Amazon ECR.
2. Deploy the application to an EKS cluster.
3. Access the application via the Load Balancer URL.

Deployment Process

• DinD Deployment: Used Docker-in-Docker for building and testing the application.
• GitHub Actions: Automated CI/CD pipeline for testing and deployment.
• EKS AWS Deployment: Deployed the Docker image to an AWS EKS cluster for scalability and reliability.

Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes and push the branch.
4. Open a pull request.

License

This project is licensed under the MIT License.