automac 0.1.0

An all-in-one automated ML pipeline for feature engineering, optimization, and evaluation.

ML-Automator 🚀

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

🌟 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).

📂 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.

🚀 Quick Start

1. Automation at its Best

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())