Diamond Price Prediction

This repository contains an open-source application for predicting the price of diamonds using Machine Learning (ML). The project is designed for both practical applications and as a learning resource for data science and machine learning enthusiasts.

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🌟 Features

• Accurate Predictions: Get price estimates based on diamond characteristics like carat, cut, color, and clarity.
• User-Friendly Interface: Simple and intuitive web application for inputting attributes and viewing results.
• Open-Source: Fully customizable and available for contributions.

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/subhash-kr0/diamond-price-prediction.git
cd diamond-price-prediction

2. Install Dependencies

Ensure you have Python installed on your system. Then, install the required Python libraries:

pip install -r requirements.txt

3. Run the Application

Start the Flask server by running:

python application.py

The application will be available at http://127.0.0.1:5000/or localhost:5000 in your browser.

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🔧 How It Works

1. Input Features:

   • Users provide details like carat, cut, color, and clarity through a web form.

2. Machine Learning Model:

   • The input is processed using a trained regression model to predict the price.

3. Results Display:

   • The estimated price and input details are displayed dynamically.

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💂️ Project Structure

├── setup.py
├── notebooks
├── application.py        # Flask application
├── static/               # CSS, JavaScript, and images
├── templates/            # HTML templates
├── src/                  # Trained machine learning model
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation

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🧠 Machine Learning Details

The ML model was trained on a dataset containing detailed information about diamonds. Key techniques include:

• Feature Engineering: Processed features like carat, cut, and color.
• Model Training: Regression model trained using Scikit-learn.
• Evaluation: Achieved high accuracy on the test dataset.

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🤝 Contributions

Contributions are welcome! Here's how you can contribute:

1. Fork the repository.
2. Create a new branch.
3. Make your changes.
4. Submit a pull request.

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📘 License

This project is licensed under the MIT License. See the LICENSE file for more details.

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✨ Acknowledgments

Special thanks to the open-source community for making tools like Python, Flask, and Scikit-learn available, and to everyone who contributes to this project!

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