AI-Aquatica

🕳️ Usage – Missing Data (AI-Aquatica)

This guide demonstrates multiple strategies for handling missing values in environmental datasets using the missing_data module.

---

1. 📦 Importing

from ai_aquatica.missing_data import (
    fill_missing_with_mean,
    fill_missing_with_median,
    fill_missing_with_mode,
    fill_missing_with_knn,
    fill_missing_with_regression,
    fill_missing_with_autoencoder
)

---

2. 🧪 Sample dataset with missing values

import pandas as pd
import numpy as np

data = pd.DataFrame({
    'pH': [7.1, 6.9, np.nan, 7.3, 7.0],
    'NO3': [1.5, np.nan, 1.7, 1.6, 1.8]
})

---

3. 📊 Simple statistical imputations

Fill with mean

mean_filled = fill_missing_with_mean(data)

Fill with median

median_filled = fill_missing_with_median(data)

Fill with mode

mode_filled = fill_missing_with_mode(data)

---

4. 🤖 AI/ML imputations

K-Nearest Neighbors

knn_filled = fill_missing_with_knn(data, n_neighbors=3)

Regression Imputation

regression_filled = fill_missing_with_regression(data)

Autoencoder Neural Network

autoencoder_filled = fill_missing_with_autoencoder(data)

---

📘 Notes

• Make sure the data contains only numerical features for ML methods.
• Autoencoder requires a basic imputation to start training.
• KNN and regression approaches benefit from scaled data.

This site is open source. Improve this page.