Welcome to the Diamond Price Prediction project! This repository focuses on predicting diamond prices using machine learning models. The project is equipped with an end-to-end deployment setup, allowing users to access predictions through a Flask web application.

• artifacts: Contains serialized data and model files.
• logs: Stores log files for training and application activities.
• notebooks: Jupyter notebooks for exploratory data analysis (EDA), model training, and dataset exploration.
• src: Source code for the project.
  • components: Modules for data ingestion, preprocessing, and model definition.
  • pipelines: Pipelines for training and making predictions.
  • utils.py: Utility functions.
  • logger.py: Logging configuration.
  • exception.py: Custom exception handling.
• templates: HTML templates for the web application.
• application.py: Flask application for serving the web pages.
• requirements.txt: Dependencies needed for the project.
• setup.py: Setup file to install project dependencies.

1. Run the setup file to install all dependencies.

   python setup.py install

2. Run the training pipeline to train the model.

   python src/pipelines/training_pipeline.py

3. Start the Flask application.

   python application.py

4. Visit http://localhost:5000/ in your browser to access the Diamond Price Prediction web application.

• GreatClasher

This project is licensed under the MIT License. Feel free to use, modify, and distribute the code. If you make significant improvements, consider contributing back to the project.

Happy predicting! 🚀