AI-Aquatica

🧼 Usage – Data Cleaning (AI-Aquatica)

This guide demonstrates how to use the data_cleaning module to prepare and clean water quality datasets before applying modeling or visualization.

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1. 📦 Importing

from ai_aquatica.data_cleaning import (
    remove_duplicates,
    handle_missing_values,
    normalize_data,
    standardize_data
)

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2. 🧪 Example dataset

import pandas as pd
import numpy as np

# Example dataset with missing values and duplicates
data = pd.DataFrame({
    'pH': [7.0, 6.8, np.nan, 7.2, 7.0],
    'NO3': [1.5, 1.7, 1.6, np.nan, 1.5]
})

# Add duplicate row
data.loc[5] = data.loc[0]

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3. ❌ Remove duplicates

data_no_duplicates = remove_duplicates(data)

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4. 🕳️ Fill missing values

Mean strategy

data_filled_mean = handle_missing_values(data, strategy='mean')

Median strategy

data_filled_median = handle_missing_values(data, strategy='median')

Interpolation

data_filled_interpolated = handle_missing_values(data, strategy='interpolate')

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5. 📏 Normalize data (range [0, 1])

data_normalized = normalize_data(data_filled_mean)

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6. 📊 Standardize data (Z-score)

data_standardized = standardize_data(data_filled_mean)

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🔎 Notes

• All functions return pandas DataFrames.
• Make sure to handle missing values before normalizing or standardizing.
• Works on numerical data only.

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