Merge branch 'master'

Author: simR122

ALL+MATERIALS+-+1 (1).zip

@@ -1,2 +1,86 @@
-![alt text](image.png) keys
-LIVE at: http://a4d76dacd85884eaaa2c0f1b1e173b67-1601615365.us-east-1.elb.amazonaws.com
+# Hotel Reservation Prediction
+
+![Python](https://img.shields.io/badge/Python-3.9-blue?logo=python&logoColor=white)
+![Flask](https://img.shields.io/badge/Flask-2.0.3-green?logo=flask&logoColor=white)
+![Docker](https://img.shields.io/badge/Docker-Containerization-blue?logo=docker&logoColor=white)
+![AWS](https://img.shields.io/badge/AWS-Cloud-orange?logo=amazon-aws&logoColor=white)
+![Jenkins](https://img.shields.io/badge/Jenkins-CI%2FCD-red?logo=jenkins&logoColor=white)
+![LightGBM](https://img.shields.io/badge/LightGBM-Model-green?logo=lightgbm&logoColor=white)
+
+## Live Application
+**Try before I am out of AWS credits**: [Hotel Reservation Prediction](http://a4d76dacd85884eaaa2c0f1b1e173b67-1601615365.us-east-1.elb.amazonaws.com)
+
+## 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
+
+### Prerequisites
+- Python 3.9+
+- Docker
+- AWS CLI
+- kubectl
+- Jenkins (optional for CI/CD)
+
+### Installation
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/your-repo/hotel-reservation-prediction.git
+   cd hotel-reservation-prediction
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Build the Docker image:
+   ```bash
+   docker build -t flask-app .
+   ```
+
+4. Run the Docker container:
+   ```bash
+   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.