A lightweight, production-ready automated machine learning library that simplifies the entire ML pipeline from data preprocessing to model deployment.

🎬 Demo

AutoML Lite in Action

Generated HTML Reports

Weights & Biases Integration

🚀 Quick Start

Installation

pip install automl-lite

5-Line ML Pipeline

python

from automl_lite import AutoMLite
import pandas as pd

# Load your data
data = pd.read_csv('your_data.csv')

# Initialize AutoML (zero configuration!)
automl = AutoMLite(time_budget=300)

# Train and get the best model
best_model = automl.fit(data, target_column='target')

# Make predictions
predictions = automl.predict(new_data)

✨ Key Features

🧠 Intelligent Automation

Auto Feature Engineering: 11.6x feature expansion (20→232 features)
Smart Model Selection: Tests 15+ algorithms automatically
Hyperparameter Optimization: Uses Optuna for efficient tuning
Ensemble Methods: Automatic voting classifiers

🏭 Production-Ready

Deep Learning: TensorFlow and PyTorch integration
Time Series: ARIMA, Prophet, LSTM forecasting
Advanced Interpretability: SHAP, LIME, permutation importance
Experiment Tracking: MLflow, W&B, TensorBoard
Interactive Dashboards: Real-time monitoring

📊 Comprehensive Reporting

Interactive HTML Reports: Beautiful visualizations
Model Performance Analysis: Confusion matrices, ROC curves
Feature Importance: Detailed analysis and correlations
Training History: Complete logs and metrics

🎯 Supported Problem Types

✅ Classification (Binary & Multi-class)
✅ Regression
✅ Time Series Forecasting
✅ Deep Learning Tasks

🔥 Performance Metrics

Production Demo Results

Training Time: 391.92 seconds for complete pipeline
Best Model: Random Forest (80.00% accuracy)
Feature Engineering: 20 → 232 features (11.6x expansion)
Feature Selection: 132/166 features intelligently selected
Hyperparameter Optimization: 50 trials with Optuna

🛠️ Advanced Usage

Custom Configuration

python

config = {
    'time_budget': 600,
    'max_models': 20,
    'cv_folds': 5,
    'feature_engineering': True,
    'ensemble_method': 'voting',
    'interpretability': True
}

automl = AutoMLite(**config)

Time Series Forecasting

python

automl = AutoMLite(problem_type='time_series')
model = automl.fit(data, target_column='sales', date_column='date')
forecast = automl.predict_future(periods=30)

Deep Learning

python

automl = AutoMLite(
    include_deep_learning=True,
    deep_learning_framework='tensorflow'
)
model = automl.fit(data, target_column='target')

📈 CLI Interface

bash

# Basic usage
automl-lite train data.csv --target target_column

# With custom config
automl-lite train data.csv --target target_column --config config.yaml

# Generate report
automl-lite report --model model.pkl --output report.html

🎨 Interactive Dashboard

python

from automl_lite.ui import launch_dashboard
launch_dashboard(automl)

🔍 Model Interpretability

python

# Get SHAP values
shap_values = automl.explain_model(X_test)

# Feature importance
importance = automl.get_feature_importance()

# Partial dependence plots
automl.plot_partial_dependence('feature_name')

🎯 Use Cases

Perfect For:

🏢 Data Scientists - Rapid prototyping
🚀 ML Engineers - Production development
📊 Analysts - Quick insights
🎓 Students - Learning ML concepts
🏭 Startups - Fast MVP development

Industries:

Finance: Credit scoring, fraud detection
Healthcare: Disease prediction, monitoring
E-commerce: Segmentation, forecasting
Marketing: Campaign optimization
Manufacturing: Predictive maintenance

🔧 Configuration Templates

Basic: Quick experiments
Production: Production deployment
Research: Extensive search
Customer Churn: Churn prediction
Fraud Detection: Fraud detection
House Price: Real estate prediction

📦 Installation Options

From PyPI (Recommended)

pip install automl-lite

From Source

bash

git clone https://github.com/Sherin-SEF-AI/AutoML-Lite.git
cd AutoML-Lite
pip install -e .

🤝 Contributing

We welcome contributions! Here's how you can help:

Fork the repository
Create a feature branch
Make your changes
Add tests
Submit a pull request

📚 Documentation & Resources

📖 Full Documentation: GitHub Wiki
🎯 API Reference: API Docs
📝 Examples: Example Notebooks
🚀 Quick Start: Installation Guide

💬 Join the Community

🌟 Star the Repository: GitHub
🐛 Report Issues: Issue Tracker
💡 Feature Requests: Discussions
📧 Contact: sherin.joseph2217@gmail.com

🏆 Why Choose AutoML Lite?

Feature	AutoML Lite	Other Libraries
Setup Time	30 seconds	30+ minutes
Configuration	Zero required	Complex configs
Production Ready	✅ Built-in	❌ Manual setup
Deep Learning	✅ Integrated	❌ Separate setup
Time Series	✅ Native support	❌ Limited
Interpretability	✅ Advanced	❌ Basic
Experiment Tracking	✅ Multi-platform	❌ Limited
Interactive Reports	✅ Beautiful HTML	❌ Basic plots

🎯 Ready to Transform Your ML Workflow?

Stop spending hours on boilerplate code. Start building amazing ML models in minutes!

pip install automl-lite

Try it now and see the difference! 🚀

Built with ❤️ by the AutoML Lite community

Tags: #python #machinelearning #automl #datascience #ml #ai #automation #productivity #opensource #deeplearning #timeseries #interpretability #experimenttracking #production #deployment

SherinJosephRoy/automl-lite

AutoML Lite 🤖

🎬 Demo

AutoML Lite in Action

Generated HTML Reports

Weights & Biases Integration

🚀 Quick Start

Installation

5-Line ML Pipeline

✨ Key Features

🧠 Intelligent Automation

🏭 Production-Ready

📊 Comprehensive Reporting

🎯 Supported Problem Types

🔥 Performance Metrics

Production Demo Results

🛠️ Advanced Usage

Custom Configuration

Time Series Forecasting

Deep Learning

📈 CLI Interface

🎨 Interactive Dashboard

🔍 Model Interpretability

🎯 Use Cases

Perfect For:

Industries:

🔧 Configuration Templates

📦 Installation Options

From PyPI (Recommended)

From Source

🤝 Contributing

📚 Documentation & Resources

💬 Join the Community

🏆 Why Choose AutoML Lite?

🎯 Ready to Transform Your ML Workflow?

SherinJosephRoy/
automl-lite