Metadata-Version: 2.1
Name: py_predpurchase
Version: 0.1.0
Summary: ```py_predpurchase```is a package for predicting online shopper purchasing intentions, containing functions to aid with data analysis processes including conducting data preprocessing as well as calculating classification metrics, cross validation scores and feature importances.The package features functions that focus mainly on analyzing the data and evaluating model performance.
License: MIT
Author: Nour Abdelfattah, Sana Shams, Calvin Choi, Sai Pusuluri
Requires-Python: >=3.9,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: click (>=8.1.7,<9.0.0)
Requires-Dist: numpy (>=1.26.4,<2.0.0)
Requires-Dist: pandas (>=2.2.2,<3.0.0)
Requires-Dist: scikit-learn (>=1.4.2,<2.0.0)
Requires-Dist: tabulate (>=0.9.0,<0.10.0)
Description-Content-Type: text/markdown

# py_predpurchase

```py_predpurchase``` is a package for predicting online shopper purchasing intentions, whether an online shopper will make a purchase from their current browsing session or not. This package contains functions to aid with the data analysis processes including conducting data preprocessing as well as calculating classification metrics, cross validation scores and feature importances.

## Installation

```bash
$ pip install py_predpurchase
```

## Usage

- TODO

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`py_predpurchase` was created by Nour Abdelfattah, Sana Shams, Calvin Choi, Sai Pusuluri. It is licensed under the terms of the MIT license.

## Credits

`py_predpurchase` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).

