Metadata-Version: 2.4
Name: universal-imputer
Version: 0.1.2
Summary: Automatic ML-based and statistical imputation for tabular data
Author: Daksh Singhal
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scikit-learn

# Universal Imputer

> Automatic imputation of missing values using statistical and machine learning methods.

---

## Features

- **Auto-detection** of numeric and categorical columns
- **Smart strategy selection** based on missing value percentage
- Multiple imputation strategies:
  - Mean / Median / Mode
  - KNN Imputation
  - Random Forest / MLP models
- Full support for mixed data types

---

## Installation

```bash
pip install universal-imputer
```

---

## Usage

```python
import pandas as pd
from universal_imputer import universal_imputer

df = pd.read_csv("data.csv")
df_imputed = universal_imputer(df)
```

---

## Configuration

Make sure your `pyproject.toml` includes:

```toml
[project]
readme = "README.md"
```

---

## Author

**Daksh Singhal**
