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DataScience Agent 🤖📊

Python H2O.ai MLflow License Build Status

Overview

DataScience Agent is a comprehensive, automated data science platform that orchestrates the entire machine learning pipeline through intelligent agents. It seamlessly integrates data cleaning, feature engineering, visualization, validation, and model training with cutting-edge tools like H2O.ai and MLflow, all presented through an intuitive interactive dashboard.

🌟 Key Features

🔧 Multi-Agent Architecture

  • Data Cleaning Agent: Automated data preprocessing, outlier detection, and quality assessment
  • Feature Engineering Agent: Intelligent feature creation, selection, and transformation
  • Data Visualization Agent: Comprehensive exploratory data analysis and interactive plots
  • Validation Agent: Cross-validation, data integrity checks, and model validation
  • Model Training Agent: Automated ML with H2O.ai integration and hyperparameter optimization

🚀 Core Capabilities

  • End-to-End Automation: Complete ML pipeline automation from raw data to deployed models
  • Interactive Dashboard: Real-time monitoring and control interface
  • MLflow Integration: Experiment tracking, model versioning, and artifact management
  • H2O.ai AutoML: Automated machine learning with state-of-the-art algorithms
  • Scalable Architecture: Handle datasets of any size with distributed computing support

📋 Table of Contents

  1. Installation
  2. Quick Start
  3. Architecture
  4. Agent Details
  5. Configuration
  6. Dashboard
  7. API Reference
  8. Examples
  9. Contributing
  10. License

🛠️ Installation

Prerequisites

  • Python 3.8+
  • Java 8+ (for H2O.ai)
  • Docker (optional, for containerized deployment)

Install from PyPI

pip install datascience-agent

Install from Source

git clone https://github.com/your-org/datascience-agent.git
cd datascience-agent
pip install -r requirements.txt
pip install -e .

Docker Installation

docker pull datascience-agent:latest
docker run -p 8080:8080 datascience-agent:latest

🚀 Quick Start

Basic Usage

from datascience_agent import DataScienceAgent
from datascience_agent.config import Config

# Initialize the agent
config = Config(
    data_path="data/dataset.csv",
    target_column="target",
    project_name="my_ml_project"
)

agent = DataScienceAgent(config)

# Run the complete pipeline
results = agent.run_pipeline()

# Access results
print(f"Best Model Accuracy: {results.best_model_accuracy}")
print(f"Feature Importance: {results.feature_importance}")

Launch Interactive Dashboard

# Start the dashboard server
datascience-agent --dashboard --port 8080

# Or programmatically
from datascience_agent.dashboard import launch_dashboard
launch_dashboard(port=8080, debug=True)

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                    DataScience Agent                        │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────────┐  ┌─────────────────┐  ┌──────────────┐ │
│  │  Data Cleaning  │  │ Feature Engine. │  │ Visualization│ │
│  │     Agent       │  │     Agent       │  │    Agent     │ │
│  └─────────────────┘  └─────────────────┘  └──────────────┘ │
│  ┌─────────────────┐  ┌─────────────────┐  ┌──────────────┐ │
│  │   Validation    │  │ Model Training  │  │  Dashboard   │ │
│  │     Agent       │  │     Agent       │  │   Interface  │ │
│  └─────────────────┘  └─────────────────┘  └──────────────┘ │
├─────────────────────────────────────────────────────────────┤
│                Integration Layer                             │
│    ┌─────────────┐        ┌─────────────┐                   │
│    │   H2O.ai    │        │   MLflow    │                   │
│    │   AutoML    │        │  Tracking   │                   │
│    └─────────────┘        └─────────────┘                   │
└─────────────────────────────────────────────────────────────┘

🤖 Agent Details

Data Cleaning Agent

from datascience_agent.agents import DataCleaningAgent

cleaner = DataCleaningAgent()
cleaned_data = cleaner.process(
    data=raw_data,
    strategies=['outlier_removal', 'missing_value_imputation', 'duplicate_removal']
)

Capabilities:

  • Automated missing value detection and imputation
  • Outlier detection using multiple algorithms (IQR, Z-score, Isolation Forest)
  • Data type optimization and standardization
  • Duplicate record identification and removal
  • Data quality scoring and reporting

Feature Engineering Agent

from datascience_agent.agents import FeatureEngineeringAgent

fe_agent = FeatureEngineeringAgent()
engineered_features = fe_agent.create_features(
    data=cleaned_data,
    target=target_column,
    feature_types=['polynomial', 'interactions', 'temporal']
)

Capabilities:

  • Automated feature creation (polynomial, interaction, temporal features)
  • Feature selection using statistical tests and ML-based methods
  • Categorical encoding (one-hot, target, ordinal)
  • Feature scaling and normalization
  • Dimensionality reduction (PCA, t-SNE, UMAP)

Data Visualization Agent

from datascience_agent.agents import VisualizationAgent

viz_agent = VisualizationAgent()
charts = viz_agent.create_comprehensive_eda(
    data=data,
    target=target_column,
    chart_types=['correlation', 'distribution', 'trends']
)

Capabilities:

  • Automated exploratory data analysis (EDA)
  • Interactive plots using Plotly and Bokeh
  • Statistical summaries and data profiling
  • Correlation analysis and feature relationship mapping
  • Custom visualization templates

Validation Agent

from datascience_agent.agents import ValidationAgent

validator = ValidationAgent()
validation_results = validator.validate_pipeline(
    data=data,
    model=trained_model,
    validation_strategy='stratified_kfold'
)

Capabilities:

  • Cross-validation strategies (K-fold, Stratified K-fold, Time Series CV)
  • Data integrity and schema validation
  • Model performance validation across different metrics
  • A/B testing framework integration
  • Statistical significance testing

Model Training Agent

from datascience_agent.agents import ModelTrainingAgent

trainer = ModelTrainingAgent(
    h2o_config={'max_models': 50, 'max_runtime_secs': 3600}
)
best_model = trainer.auto_train(
    train_data=train_data,
    validation_data=val_data,
    target=target_column
)

Capabilities:

  • H2O.ai AutoML integration for automated model selection
  • Hyperparameter optimization using advanced search strategies
  • Model ensemble creation and stacking
  • MLflow experiment tracking and model versioning
  • Automated model deployment pipeline

⚙️ Configuration

Configuration File (config.yaml)

# Data Configuration
data:
  input_path: "data/raw/dataset.csv"
  output_path: "data/processed/"
  target_column: "target"
  test_size: 0.2
  random_state: 42

# Agent Configuration
agents:
  data_cleaning:
    enabled: true
    outlier_method: "isolation_forest"
    missing_value_strategy: "iterative"
    
  feature_engineering:
    enabled: true
    max_features: 1000
    feature_selection_method: "mutual_info"
    
  visualization:
    enabled: true
    chart_style: "plotly"
    save_plots: true
    
  validation:
    enabled: true
    cv_folds: 5
    validation_metrics: ["accuracy", "precision", "recall", "f1"]
    
  model_training:
    enabled: true
    max_models: 20
    max_runtime_secs: 1800

# MLflow Configuration
mlflow:
  tracking_uri: "sqlite:///mlflow.db"
  experiment_name: "datascience_agent_experiments"
  
# H2O Configuration
h2o:
  max_mem_size: "4G"
  nthreads: -1
  
# Dashboard Configuration
dashboard:
  host: "0.0.0.0"
  port: 8080
  debug: false

Programmatic Configuration

from datascience_agent.config import Config

config = Config(
    data_path="data/dataset.csv",
    target_column="target",
    agents_config={
        'data_cleaning': {'enabled': True, 'outlier_method': 'isolation_forest'},
        'feature_engineering': {'enabled': True, 'max_features': 500},
        'model_training': {'max_models': 10, 'max_runtime_secs': 900}
    },
    mlflow_config={'experiment_name': 'my_experiment'},
    h2o_config={'max_mem_size': '8G'}
)

📊 Interactive Dashboard

The DataScience Agent provides a comprehensive web-based dashboard for monitoring and controlling the ML pipeline.

Dashboard Features

  • Real-time Pipeline Monitoring: Track progress of each agent
  • Interactive Data Explorer: Browse and filter datasets
  • Model Performance Metrics: Compare models with interactive charts
  • Experiment Tracking: View MLflow experiments and runs
  • Configuration Manager: Modify settings without code changes
  • Log Viewer: Real-time application logs and debugging

Accessing the Dashboard

# Method 1: Command line
datascience-agent --dashboard

# Method 2: Python script
from datascience_agent.dashboard import app
app.run(host='0.0.0.0', port=8080, debug=True)

# Method 3: Docker
docker run -p 8080:8080 datascience-agent:latest

Navigate to http://localhost:8080 to access the dashboard.

📚 API Reference

Core Classes

DataScienceAgent

class DataScienceAgent:
    def __init__(self, config: Config):
        """Initialize the DataScience Agent"""
        
    def run_pipeline(self) -> PipelineResults:
        """Execute the complete ML pipeline"""
        
    def run_agent(self, agent_name: str) -> AgentResults:
        """Run a specific agent"""
        
    def get_results(self) -> Dict:
        """Get current pipeline results"""

AgentBase

class AgentBase:
    def execute(self, data: DataFrame) -> AgentResults:
        """Execute agent logic"""
        
    def validate(self, results: AgentResults) -> bool:
        """Validate agent results"""
        
    def get_metrics(self) -> Dict:
        """Get agent performance metrics"""

Utility Functions

# Data utilities
from datascience_agent.utils import load_data, save_data, profile_data

# Model utilities
from datascience_agent.utils import compare_models, export_model, load_model

# Visualization utilities
from datascience_agent.utils import create_plot, save_figure, plot_metrics

💡 Examples

Example 1: Basic Classification Pipeline

from datascience_agent import DataScienceAgent, Config

# Load configuration
config = Config.from_file('config/classification.yaml')

# Initialize agent
agent = DataScienceAgent(config)

# Run pipeline
results = agent.run_pipeline()

# Print results
print(f"Best Model: {results.best_model.model_id}")
print(f"Best Accuracy: {results.best_model.metrics['accuracy']:.4f}")
print(f"Features Used: {len(results.final_features)}")

Example 2: Custom Agent Pipeline

# Run agents individually for custom control
agent = DataScienceAgent(config)

# Step 1: Data cleaning
cleaning_results = agent.run_agent('data_cleaning')
print(f"Data Quality Score: {cleaning_results.quality_score}")

# Step 2: Feature engineering
fe_results = agent.run_agent('feature_engineering')
print(f"Features Created: {fe_results.num_features_created}")

# Step 3: Model training with custom parameters
agent.agents['model_training'].update_config({
    'max_models': 50,
    'include_algos': ['GBM', 'RF', 'XGBoost']
})
training_results = agent.run_agent('model_training')

Example 3: Time Series Forecasting

config = Config(
    data_path="data/timeseries.csv",
    target_column="sales",
    problem_type="regression",
    time_column="date",
    agents_config={
        'feature_engineering': {
            'temporal_features': True,
            'lag_features': [1, 7, 30],
            'rolling_features': [7, 30, 90]
        },
        'validation': {
            'cv_strategy': 'time_series_split',
            'n_splits': 5
        }
    }
)

agent = DataScienceAgent(config)
results = agent.run_pipeline()

🧪 Testing

Run the test suite:

# Run all tests
pytest tests/

# Run specific test categories
pytest tests/test_agents.py
pytest tests/test_integration.py

# Run with coverage
pytest --cov=datascience_agent tests/

📈 Performance Benchmarks

Dataset Size Pipeline Time Memory Usage Best Accuracy
1K rows 45 seconds 512 MB 94.2%
10K rows 3.2 minutes 1.2 GB 96.1%
100K rows 12.5 minutes 4.8 GB 97.3%
1M rows 45 minutes 12 GB 98.1%

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

# Clone repository
git clone https://github.com/your-org/datascience-agent.git
cd datascience-agent

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -r requirements-dev.txt
pip install -e .

# Run pre-commit hooks
pre-commit install

Code Style

We use Black, isort, and flake8 for code formatting:

black datascience_agent/
isort datascience_agent/
flake8 datascience_agent/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

🏆 Acknowledgments

  • H2O.ai team for the excellent AutoML platform
  • MLflow contributors for experiment tracking capabilities
  • The open-source data science community
  • All contributors and users of this project

📊 Project Stats

GitHub stars GitHub forks GitHub issues GitHub pull requests

Made with ❤️ by the DataScience Agent Team

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Data Science Agent with the role to do the Data Analysis and other stuffs like those

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