Metadata-Version: 2.4
Name: agro-data-intelligence
Version: 0.1.0
Summary: Agricultural data auditing, visualization and explainable machine learning framework
Author: Md. Noman
Project-URL: Homepage, https://github.com/example/agro-data-intelligence
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.5.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: shap>=0.40.0
Dynamic: license-file

# Data Audit

`data_audit` is a powerful Pandas accessor that makes auditing, cleaning, and training ML models on tabular data seamless and intuitive.

## Features

- **Data Auditing**: Rapidly scan dataframes for missing values, duplicates, and outliers (via IQR/Z-score/Custom Bounds).
- **Auto-Fixing**: Heal data in-place or generate suggestions.
- **Anomaly Detection**: Out-of-the-box anomaly detection via Isolation Forest.
- **Embedded ML**: Instantly train Regression or Classification models (Random Forest) directly on your dataframe.
- **Explainability**: SHAP integration for global feature importance and local predictions.

## Installation

```bash
pip install data_audit
```

## Quickstart

```python
import pandas as pd
import data_audit

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

# 1. Scan for issues
issues = df.audit.scan()

# 2. Fix issues automatically
df.audit.fix(mode='auto')

# 3. Train a Machine Learning Model on a target column
df.audit.ml.train(target="SalePrice")

# 4. Explain the model's global and local features
print(df.audit.ml.explain())
```
