The pipeline encompasses data preprocessing, feature engineering, model training, and evaluation stages. By employing machine learning algorithms and leveraging data-driven insights, the team aims to provide real-time pricing estimates for diamonds based on their unique attributes.
This project was generated using Kedro 0.18.10
.
The project follows a structured approach as follows:
diamond-price-prediction/
├── conf/
│ ├── base/
│ │ └──parameters/
│ │ │ └── data_processing.yml
│ │ │ └── data_science.yml
│ │ ├── catalog.yml
│ │ ├── logging.yml
│ │ └── parameters.yml
│ ├── local/
│ │ └── ...
│
├── data/
│ ├── 01_raw/
│ │ └── diamonds.csv
│ ├── 02_intermediate/
│ │ ├── outlier_removal_diamonds.pq
│ │ └── final_diamonds.pq
│ ├── 03_primary/
│ │ └── diamonds_preprocessed.pq
│ ├── 04_feature/
│ │ └── ...
│ ├── 05_model_input/
│ │ └── ...
│ ├── 06_models/
│ │ ├── regressor.pickle/
│ └── 07_model_output/
│ └── X_test.pq
│ └── X_train.pq
│ └── y_test.csv
│ └── y_train.csv
├── docs/
│ └──source/
│ │ └──conf.py
│ │ └──index.rst
├── img/
│ └── pipeline.png
├── notebooks/
│ └── Diamonds.ipynb
├── logs/
│ └── info.log
├── src/
│ ├── diamond_price_prediction/
│ │ ├── pipelines/
│ │ └── data_preprocessing/
│ │ └── nodes.py
│ │ └── pipeline.py
│ │ └── data_science/
│ │ └── nodes.py
│ │ └── pipeline.py
│ │ └── dataviz.py
│ │ └── pipeline_registry.py
│ │ └── settings.py
│ │ └── requirements.txt
│ │ └── setup.py
│ └── ...
├── tests/
│ ├── __init__.py
│ ├── test_run.py
│ └── ...
├── .gitignore
├── LICENSE
├── pyproject.toml
├── Dockerfile
├── README.md
├── setup.cfg
To run the diamond price pipeline using docker, follow these steps:
-
Install Docker on your machine. You can download Docker from the official website: https://www.docker.com/
-
Pull the Docker image for the diamond price prediction project from Docker Hub using the following command:
docker pull <your-user-name>/diamond-price-prediction
Once the image is downloaded, tou can run a Docker container with the following command:
docker run -it <your-user-name>/diamond-price-prediction
Inside the Docker container, navigate to the project directory:
cd diamond-price-prediction
Install the project dependencies by running:
pip install -r src/requirements.txt
Run the pipeline using the command:
kedro run
Visualize the pipeline using:
kedro viz
This pipeline is divided in two main steps: data preprocessing and model training pipelines (both inside src/pipelines)
Once the model is successfully trained and evaluated, it can be deployed into GemExquisite's pricing system, enabling real-time or batch price estimation for diamonds. This will enhance the customer experience and establish GemExquisite as a trusted provider of high-quality diamonds with transparent pricing information.
Contributions to this project are welcome. If you have any suggestions or improvements, please feel free to contact me.
This project is licensed under the MIT License.
For inquiries or further information, please contact me at:
- Email: [email protected]
- LinkedIn: https://www.linkedin.com/in/pedro-a-d-s/