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# ------------------------------------------------- # 2. Scale data to [0,1] # ------------------------------------------------- scaler = MinMaxScaler() scaled = scaler.fit_transform(df[['count']])

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3.3. Cloud / Edge AI

  • Platform Options: Google Cloud IoT Core, AWS IoT Greengrass, Azure IoT Hub, or an on‑premise Open‑Source stack (K3s + EMQX).
  • ML Pipelines:

    Do you need help removing a suspicious file you already downloaded? Platform Options: Google Cloud IoT Core, AWS IoT

    8. Sample Code Snippet – Real‑Time Occupancy Forecast (Python + TensorFlow)

    import pandas as pd
    import tensorflow as tf
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import LSTM, Dense
    from sklearn.preprocessing import MinMaxScaler
    

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    Conclusion

    • Recap the potential benefits of integrating ML into public toilet management and design.
    • Encourage stakeholders to consider the integration of smart technologies in public facilities for a better public experience.