Metadata-Version: 2.4
Name: automac
Version: 0.1.0
Summary: An all-in-one automated ML pipeline for feature engineering, optimization, and evaluation.
Home-page: https://github.com/yourusername/automater
Author: Abhishek Sharma
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
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: category_encoders
Requires-Dist: optuna
Requires-Dist: catboost
Requires-Dist: xgboost
Requires-Dist: lightgbm
Requires-Dist: plotly
Requires-Dist: kaleido
Requires-Dist: openpyxl
Dynamic: author
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# ML-Automator ðŸš€

**ML-Automator** is a powerful, low-code machine learning utility library that automates Feature Engineering, Hyperparameter Optimization, and Model Evaluation.


## ðŸŒŸ Features

- **Automated Feature Engineering**: Handle Target Encoding, Scaling, and Imputation in one line.
- **Optuna-Powered Optimization**: Pre-configured search spaces for RandomForest, XGBoost, CatBoost, LightGBM, and more.
- **Deep Evaluation**:
    - Multi-model score comparison.
    - Interactive ROC and Calibration curves using Plotly.
    - Automated Learning Curve analysis.
    - Automatic report generation (CSV/Excel/PNG).

## ðŸ“‚ Project Structure
Your library is organized into three core modules:
1. `feature_engineering.py`: Data preprocessing and importance extraction.
2. `models_optimizer.py`: Optuna-based hyperparameter tuning.
3. `trainer.py`: Model training, cross-validation, and visualization.

## ðŸš€ Quick Start

### 1. Automation at its Best
```python
from automater.feature_engineering import FeatureEvaluation
from sklearn.ensemble import RandomForestClassifier

evaluator = FeatureEvaluation(X, y)
processed_x, importance_df = evaluator.fit_all_at_once(RandomForestClassifier())
