This web application allows users to predict the price of diamonds based on various attributes and quality factors. It provides accurate price estimations by leveraging advanced machine learning algorithms and a comprehensive dataset of diamond characteristics.
- Predict the price of diamonds based on carat, depth, table, dimensions, cut, color, and clarity.
- Utilizes a trained machine learning model to provide accurate price estimations.
- User-friendly interface for entering diamond attributes and obtaining predictions.
- Real-time results displayed instantly after submitting the form.
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Clone the repository:
git clone https://github.com/navneetguptacse/diamond-price-prediction.git -
Install the required dependencies:
pip install -r requirements.txt -
Run the application:
python app.py -
Access the application by visiting
http://localhost:5000in your web browser.
- Fill in the form with the relevant attributes of the diamond, including carat, depth, table, dimensions (x, y, z), cut, color, and clarity.
- Click the "Submit" button to predict the price of the diamond.
- The predicted price will be displayed on the screen in real-time.
- Python
- Flask
- Scikit-learn
- HTML
- CSS
- JavaScript
The diamond dataset used for training the model is sourced from [source-name]. It consists of a wide range of diamonds with various attributes and corresponding prices.
This project is licensed under the iNeuron.ai.
We would like to express our gratitude to the following individuals and resources for their valuable contributions to the Diamond Price Prediction project:
- The Diamond Institute for providing valuable insights and domain expertise in the field of diamond evaluation.
- The open-source community for developing and maintaining the essential frameworks and libraries used in this project.
- The contributors and users of the diamond dataset [source-name] for making the dataset publicly available, enabling us to train and test our machine learning model.