This project aims to predict the price of diamonds based on various features using machine learning techniques. The model is built, trained, and evaluated to provide accurate price predictions for diamonds.
Diamonds come in various shapes, sizes, and qualities, influencing their market value. This project employs machine learning algorithms to predict diamond prices, allowing users to estimate the cost of a diamond based on its characteristics.
- Python (3.x recommended)
- Jupyter Notebooks
- Dependencies (see requirements.txt)
-
Clone the repository:
git clone [https://github.com/wiznikvibe/diamond_price_prediction.git]
-
Install dependencies:
pip install -r requirements.txt
The dataset used for this project contains information about various diamonds, including carat weight, cut, color, clarity, and price. The dataset is available in the data directory.
To use the model for predictions, refer to notebooks/Exploration.ipynb. This notebook provides a guide on how to load the model and make predictions on new data.
Contributions are welcome! For major changes, please open an issue first to discuss the proposed changes.
This project is licensed under the MIT License.
- The dataset used in this project is sourced from