Metadata-Version: 2.4
Name: autopieby2
Version: 1.0.82
Summary: AutoImpute - Missing Data Imputation Framework for Machine Learning
Home-page: https://github.com/pieby2/AutoImpute
Author: Prakhar
Author-email: Prakharpragyan1000@gmail.com
License: MIT
Keywords: data science,machine learning,data preprocessing,null imputation,predictive null imputation,multiple null imputation,automated machine learning
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Customer Service
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Telecommunications Industry
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: atlantic>=1.1.80
Requires-Dist: catboost>=1.1.1
Requires-Dist: xgboost>=1.7.3
Requires-Dist: lightgbm>=3.3.5
Requires-Dist: matplotlib>=3.5.0
Requires-Dist: seaborn>=0.11.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
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# AutoImpute

**AutoImpute - Missing Data Imputation Framework for Machine Learning**

AutoImpute is a comprehensive Python library designed to simplify and automate the process of handling missing data in machine learning datasets. It provides a suite of imputation strategies, from simple statistical methods to advanced predictive modeling, ensuring your data is ready for analysis.

## Features

*   **Diverse Imputation Strategies**: Supports mean, median, mode, constant, and predictive imputation (using models like XGBoost, CatBoost, LightGBM).
*   **Automated Workflow**: Streamlines the imputation process, fitting seamlessly into scikit-learn pipelines.
*   **Evaluation Metrics**: Includes tools to evaluate imputation quality and impact on model performance.
*   **Visualization**: Visualize missing data patterns and imputation results.

## Installation

```bash
pip install autopieby2
```

## Usage

```python
from autoimpute.imputation import AutoImputer
import pandas as pd

# Load your data
data = pd.read_csv("your_data.csv")

# Initialize and fit imputer
imputer = AutoImputer()
imputed_data = imputer.fit_transform(data)

print(imputed_data.head())
```

## License

MIT License
