Metadata-Version: 2.4
Name: ml-qol
Version: 0.1.7
Summary: An ML toolkit package that provides quality-of-life features
Author-email: Mashrur Sakif Souherdo <mashrursakifsouehrdo@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Mashrur Sakif Souherdo
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: catboost
Requires-Dist: lightgbm
Requires-Dist: xgboost
Requires-Dist: matplotlib
Requires-Dist: seaborn
Dynamic: license-file

# ML QOL

**ML QOL** is a Python package that provides helper functions and quality-of-life features for machine learning tasks

## Features

- Automated hyperparameter mapping for different models such as CatBoost and LightGBM
- Data handling functions for managing dates, NaN values, and more
- Feature engineering functions such as combining features together or adding target encoded features
- Fast and easy way to train and compare different models and their performance, e.g, feature importance, confusion matrix
- Perform folded training and gathering averaged predictions
- Perform weighted ensembling with different types of models

## Dependencies

This package relies on the following Python libraries:

- [pandas](https://pandas.pydata.org/)
- [numpy](https://numpy.org/)
- [scikit-learn](https://scikit-learn.org/)
- [lightgbm](https://github.com/microsoft/LightGBM)
- [catboost](https://catboost.ai/)
- [xgboost](https://github.com/dmlc/xgboost)
- [matplotlib](https://matplotlib.org/)
- [seabron](https://seaborn.pydata.org/)

You can install them via pip:

```bash
pip install pandas numpy scikit-learn lightgbm catboost xgboost matplotlib seaborn
```

## Installation

**Using pip**

```bash
pip install ml-qol
```

## Quick Start

```python
from ml_qol import train_model

# Train a model
model = train_model('lightgbm', 'regression', train_data=train_df, target_col='price')

# Show feature importances
model.plot_importance()

# Use for inference
predictions = model.predict(test_df)
print(predictions)
```

## Resources

- PyPi: (https://pypi.org/project/ml-qol)
- GitHub repository: (https://github.com/mashrursakif/ml-qol)
- Documentation and examples: (https://github.com/mashrursakif/ml-qol/tree/main/examples)

### License

#### MIT

## Author

Developed by Mashrur Sakif Souherdo - [GitHub](https://github.com/mashrursakif)
