Implemented a robust End-to-End Diamond Price Prediction system using production-grade code. Utilized AWS EC2 AWS CodePipeline Git and GitHub for seamless deployment. Applied machine learning with an automated sklearn pipeline and developed model files as a REST API using Flask for efficient integration.
http://diamondpriceprediction-env.eba-qhbi4g2r.ap-south-1.elasticbeanstalk.com/
1. To create an environment:
conda create -p venv python==3.8
2. When conda asks you to proceed type y:
proceed ([y]/n)?
3. To create an environment with a specific version of Python:
conda create -n myenv python=3.8
4. Install requirements.txt
pip install -r requirements.txt
5. Run Flask Application
python application.py
Technologies used in the project:
- Python
- conda
- pandas
- Numpy
- Flask
- Seaborn
- scikit-learn
- Modular code
- Git
- GIthub
- AWS EC2
- AWS Codepipelines
This project is licensed under the MIT License