AI-Aquatica

📏 Usage – Data Standardization (AI-Aquatica)

This guide demonstrates how to use the data_standardization module to transform your data for better performance in machine learning models.

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1. 📦 Importing

from ai_aquatica.data_standardization import (
    normalize_data,
    standardize_data,
    log_transform,
    sqrt_transform,
    boxcox_transform
)

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2. 🧪 Sample data

import pandas as pd
import numpy as np

data = pd.DataFrame({
    'nitrate': [1.2, 3.4, 2.1, 5.6, 4.2],
    'phosphate': [0.5, 1.5, 0.9, 2.3, 1.8]
})

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3. 🔄 Normalize data (0–1 range)

normalized = normalize_data(data)

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4. 🧮 Standardize data (mean = 0, std = 1)

standardized = standardize_data(data)

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5. 🔢 Log transformation

log_transformed = log_transform(data)

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6. 🔳 Square root transformation

sqrt_transformed = sqrt_transform(data)

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7. 📈 Box-Cox transformation (positive values only)

boxcox_data = boxcox_transform(data)

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⚠️ Notes

• Box-Cox requires all values to be strictly positive.
• Transformations help reduce skewness and improve model convergence.
• Apply after handling missing values and before training models.

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