The water-quality-and-survival-rate-predict-ml-api project is designed to predict the survival rate of shrimp in a pond based on four key water quality variables:

• DO (Dissolved Oxygen)
• Salinity
• pH
• Water Temperature
• Total Dissolved Solids (TDS)

The project uses a Random Forest algorithm to detect anomalies in water quality and predict the shrimp survival rate (the y variable). This API is built with Python, Flask, and Scikit-learn, and is intended to be consumed by a front-end application, specifically for shrimp pond management.

To get started with the water-quality-ml-api project, follow the instructions below.

Ensure you have the following installed:

• Python 3.8+
• pip (Python package installer)
• MySQL database
• Pandas
• Scikit-learn
• Pickle
• Flask

Before running the application, you'll need to train the Random Forest model on your dataset. You can either use a real dataset or a dummy dataset for testing purposes.

1. Prepare the Dataset:

   • Ensure your dataset contains the following columns: DO, pH, Water Temperature, TDS, Salinity, and Survival Rate.

2. Train the Model:

   • Run the script to train the model:

   train_model.py

This will generate a trained Random Forest model stored in the models directory.

To start the Flask application, use the following commands:

1. Install Dependencies:

    pip install -r requirements.txt

2. Run the Application::

    python run.py

The Flask application will start and be available at http://localhost:5000.

The water-quality-ml-api exposes several endpoints to interact with the trained model and water quality data.

Predict Survival Rate:

• Endpoint: api/predict
• Method: POST
• Request Body

{
    "DO": 5.0,
    "Salinitas": 20.0,
    "pH": 7.2,
    "TDS": 250,
    "Suhu": 30.0
}

• Response

{
    "anomaly_detected": true,
    "survival_rate": 30.099853793449764
}

• Request Body

{
    "DO": 7.0,
    "Salinitas": 15.0,
    "pH": 9.0,
    "TDS": 500,
    "Suhu": 26.0
}

• Response

{
    "anomaly_detected": true,
    "survival_rate": 34.370288893660906
}

Anomaly Recommendation::

• Endpoint: api/recommendation
• Method: POST
• Request Body

{
    "DO": 7.0,
    "Salinitas": 25.0,
    "pH": 7.8,
    "TDS": 350,
    "Suhu": 27.0
}

• Response

{
    "anomaly_detected": false,
    "recommendation": [
        "Water quality is within optimal parameters. No immediate action required."
    ],
    "survival_rate": 74.54531311614866
}

• Request Body

{
    "DO": 6.0,
    "Salinitas": 40.0,
    "pH": 7.5,
    "TDS": 600,
    "Suhu": 29.0
}

• Response

{
    "anomaly_detected": true,
    "recommendation": [
        "Adjust salinity to be between 15 and 25 ppt.",
        "Adjust the pH by using pH buffer solutions."
    ],
    "survival_rate": 39.5908783319008
}

• Request Body

{
    "DO": 6.0,
    "Salinitas": 22.0,
    "pH": 7.8,
    "TDS": 300,
    "Suhu": 55.0
}

• Response

{
    "anomaly_detected": true,
    "recommendation": [
        "Adjust temperature to be between 27 and 30°C."
    ],
    "survival_rate": 33.992111805857526
}