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Augini Documentation

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AI-Powered Tabular Data Framework

Augini is a Python framework that leverages AI for data manipulation and analysis through two powerful APIs:

DataEngineer

Transform and prepare your data with automated: - Feature engineering - Data preprocessing - Dataset scaling - Data augmentation

DataAnalyzer

Extract insights from your data using: - Statistical analysis - Trend detection - Pattern recognition - Visualization integration

Quick Start

from augini import DataEngineer, DataAnalyzer
import pandas as pd

# Sample customer data
data = pd.DataFrame({
    'age': [25, 35, 45, 28, 52],
    'income': [30000, 45000, 75000, 35000, 85000],
    'purchases': [150, 450, 850, 250, 950]
})

# Initialize with your API key
engineer = DataEngineer(api_key='your-api-key')

# Generate customer segments
data = engineer.generate_feature(
    df=data,
    new_feature_name='customer_segment',
    new_feature_description='Create customer segments based on age, income, and purchases',
    output_type='category'
)

# Initialize analyzer and fit the data
analyzer = DataAnalyzer(api_key='your-api-key')
analyzer.fit(data)

# Ask questions about the data
insights = analyzer.chat(
    "What are the characteristics of different customer segments? "
    "Focus on age, income, and purchase patterns."
)

print(insights)

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