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https://github.com/orhfusion/flight-ml-model
This project demonstrates the development and deployment of a machine learning model to predict flight ticket prices.
https://github.com/orhfusion/flight-ml-model
docker-image dockerfile machine-learning model-deployment
Last synced: 20 days ago
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This project demonstrates the development and deployment of a machine learning model to predict flight ticket prices.
- Host: GitHub
- URL: https://github.com/orhfusion/flight-ml-model
- Owner: OrhFusion
- Created: 2024-08-09T10:43:27.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-10-09T05:40:41.000Z (3 months ago)
- Last Synced: 2024-12-20T00:41:49.203Z (20 days ago)
- Topics: docker-image, dockerfile, machine-learning, model-deployment
- Language: Jupyter Notebook
- Homepage:
- Size: 6.92 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
**Flight Price Prediction Model:**
## Flight Price Prediction Model
This repository showcases a sophisticated machine learning model engineered for precise prediction of flight ticket prices. By employing state-of-the-art regression techniques, this model provides a reliable tool for analyzing and forecasting flight costs based on a rich dataset of flight-related attributes.
### Overview
The model is meticulously crafted using a suite of Python libraries and tools, including Pandas for data manipulation, NumPy for numerical operations, and Scikit-learn for implementing advanced regression algorithms. It integrates a comprehensive approach to data preprocessing and feature engineering, ensuring that the model achieves high performance and accuracy.
### Key Features
- **In-Depth Data Exploration and Analysis**: The project includes thorough exploration and preprocessing of flight data. This phase involves cleaning, transforming, and visualizing the data to uncover insightful patterns and relationships that inform the model’s accuracy.
- **Advanced Regression Modeling**: Utilizes cutting-edge regression techniques to deliver precise flight price predictions. The model is fine-tuned using cross-validation and hyperparameter optimization to enhance its predictive capabilities.
- **Dockerized Deployment**: The model is containerized using Docker, enabling easy and consistent deployment across various environments. This approach ensures scalability, simplifies integration, and enhances portability, making it adaptable for use in diverse applications.
- **Integration Potential**: Designed with flexibility in mind, the model can be seamlessly integrated into various platforms, such as travel booking systems, price comparison tools, and other related applications, offering valuable insights and functionalities to end-users.
### Deployment and Usage
1. **Containerization**: The model is packaged in a Docker container, which can be easily deployed on any system with Docker installed. This setup streamlines the process of running the model and ensures consistent performance across different environments.
2. **Scalability**: Dockerization allows for straightforward scaling of the model to handle varying loads and integrate with larger systems, making it suitable for both small-scale applications and enterprise-level solutions.
3. **Access and Integration**: The Docker image is available on Docker Hub under the name `orhfusion/ticket-predict-model`, facilitating easy access and deployment for developers and data scientists looking to leverage the model in their projects.
4. **Pull docker image -- orhfusion/ticket-predict-model**
This repository not only provides a powerful tool for flight price prediction but also serves as a practical example of modern data science practices and deployment strategies.
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