Credit scoring Project to predict whether or not credit extended to an applicant will likely result in profit or losses for the lending institution. The project main is to learn MLOPS best practices. It also includes fairness and interpretability analysis.
- Clone the repository.
- Navigate to the project directory.
- Create a conda environment using the
conda.ymlfile with the following command:conda env create -f conda.yml - Activate the conda environment:
conda activate devops_project
The project uses a config.yaml file for configuration settings, including data paths, model parameters, and feature engineering settings. Modify this file as needed.
opiod_analysis/
.
├── .gitignore
├── conda.yml
├── config.yaml
├── LICENSE
├── main.py
├── Mlproject
├── README.md
├── data/
│ ├── external/
│ │ └── .gitkeep
│ ├── interim/
│ │ └── .gitkeep
│ ├── processed/
│ │ ├── .gitkeep
│ │ ├── bankloan_processed.csv
│ │ └── cleaned_data.csv
│ ├── raw/
│ │ ├── .gitkeep
│ │ └── bankloan.csv
│ └── ref/
│ └── ref_bankloan.csv
├── notebooks/
│ ├── .gitkeep
│ ├── ML Project.py
│ ├── ML Project_Refactored.py
│ └── ML Project_WandB.ipynb
├── references/
│ └── .gitkeep
├── reports/
│ ├── .gitkeep
│ └── figures/
│ └── .gitkeep
├── src/
│ ├── __init__.py
│ ├── data_check/
│ │ ├── conda.yml
│ │ ├── conftest.py
│ │ ├── Mlproject
│ │ ├── test_data.py
│ │ └── __init__.py
│ ├── data_split/
│ │ ├── conda.yaml
│ │ ├── Mlproject
│ │ ├── split_train_test.py
│ │ └── __init__.py
│ ├── exploration/
│ │ ├── conda.yaml
│ │ ├── explore_data.py
│ │ ├── Mlproject
│ │ └── __init__.py
│ ├── interpretability/
│ │ ├── conda.yaml
│ │ ├── Mlproject
│ │ ├── model_interpretability.py
│ │ └── __init__.py
│ ├── load_data/
│ │ ├── conda.yml
│ │ ├── data_load.py
│ │ ├── Mlproject
│ │ └── __init__.py
│ ├── models/
│ │ ├── conda.yaml
│ │ ├── Mlproject
│ │ ├── train_model.py
│ │ ├── transformer.py
│ │ ├── __init__.py
│ │ └── reports/
│ └── preprocessing/
│ ├── conda.yml
│ ├── Mlproject
│ └── preprocess_data.py
└── tests/
├── __init__.py
├── test_data_load.py
├── test_explore_data.py
├── test_model_interpretability.py
├── test_model_test.py
├── test_preprocess_data.py
├── test_split_train.py
├── test_train_model.py
└── data/
├── test_data.csv
├── test_data.json
├── test_data.txt
└── test_data.xlsx
- Make sure you are in the conda environment.
- Run your Python scripts or Jupyter Notebooks within this environment to ensure all dependencies are available.
Tests can be run using pytest. Make sure to install pytest if you haven't, and then run the following command from the project directory:
pytest tests/
- Interpretability is implemented using both SHAP and LIME techniques. Refer to
interpretability.pyfor more details. - Fairness metrics including demographic parity and equalized odds are calculated. Refer to
fairness_analysis.pyfor more details.
This project is licensed under the MIT License.
Project based on the cookiecutter data science project template. #cookiecutterdatascience
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