autonova 1.0.3

An automated machine learning pipeline with all-at-one-click feature.

⚡AutoNova

AutoNova is a modern AutoML framework that can handle everything from data preprocessing to model training in just 3 lines of code.
It is designed to save your time, boost productivity, and make Machine Learning workflows effortless.

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✨ Features

🔹 Preprocessing Class

• Cleaning & Transforming – Handle missing values, scaling, encoding
• Feature Engineering – Generate new useful features automatically
• Feature Selection – Select only the most important features
• Balancing – Fix imbalanced datasets with SMOTE
• Splitting – Train-test splits with ease

🔹 Model Training Class

• Automated Model Selection & Training
• Hyperparameter Tuning with Optuna
• Performance Metrics (Accuracy, Precision, Recall, F1, ROC-AUC)

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🚀 Installation

pip install autonova

📝 Usage Description

from autonova.auto import AutoNova
import pandas as pd

df = pd.read_csv("star_classification.csv")
target_col = "class"

automl = autonova(data=df, target_col=target_col)
automl.go(use_gpu=False, fast_mode=False, cv_splits=5, n_trials=50)

print("Best Model:", mode.best_model)
print("Preprocessing Steps:", mode.preprocess_logic)
print("Score:", mode.score)
print("Train Data Shapes:", [x.shape for x in mode.train_data])
print("Test Data Shapes:", [x.shape for x in mode.test_data])

• use_gpu=True GPU for Optuna training
• fast_mode=True only Faster models
• cv_splits=5 number of Splits
• n_trials=50 number of Trials

🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change. Take use of CONTRIBUTING.md file to make contributions.

📜 License

MIT License – do anything, just give credit.