rohitjakkam / diamondpriceprediction

DiamondPricePrediction

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.

🚀 Demo

http://diamondpriceprediction-env.eba-qhbi4g2r.ap-south-1.elasticbeanstalk.com/

Project Screenshots:

🛠️ Installation Steps:

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

💻 Built with

Technologies used in the project:

• Python
• conda
• pandas
• Numpy
• Flask
• Seaborn
• scikit-learn
• Modular code
• Git
• GIthub
• AWS EC2
• AWS Codepipelines

🛡️ License:

This project is licensed under the MIT License