This project provides a RESTful API to predict the quality of industrial wastewater using machine learning models. The API is built using the Flask framework, following the Clean Architecture approach. The dataset consists of various water quality parameters, including pH, conductivity, turbidity, TSS, BOD, COD, Total Coliform, and Color.

The dataset consists of the following features:

• pH: Acidity/alkalinity level.
• Conductivity: The ability of water to conduct electric current.
• Turbidity: Clarity of the water.
• TSS: Total suspended solids.
• BOD: Biochemical oxygen demand.
• COD: Chemical oxygen demand.
• Total Coliform: Indicator of bacterial contamination.
• Color: The color of the water sample. ** Target Variable You can choose any of the above features as the target variable (Y) to predict based on the other features (X).

To get started with the industrial-wastewater-quality-prediction-api project, follow the instructions below.

Ensure you have the following installed:

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

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: Conductivity, pH, TSS, Turbidity, COD, Total Coliform, Color, And BOD

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 Data 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 BOD:

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

{
    "pH": 7.2,
    "Conductivity (µS/cm)": 1200,
    "Turbidity (NTU)": 3.5,
    "TSS (mg/L)": 15,
    "COD (mg/L)": 220,
    "Total Coliform (MPN/100mL)": 50
}

• Response

{
    "anomaly_detected": false,
    "predicted_BOD": 102.6950136668083
}

Get Water Quality Data::

• Endpoint: api/data
• Method: GET
• Response

[
    {
        "BOD (mg/L)": 96.87674072417535,
        "COD (mg/L)": 463.8309450955251,
        "Conductivity (µS/cm)": 3286.608315717363,
        "Date": "Sun, 01 Jan 2023 00:00:00 GMT",
        "TSS (mg/L)": 78.43473346450703,
        "Total Coliform (MPN/100mL)": 3597.517592938975,
        "Turbidity (NTU)": 134.5706704982383,
        "pH": 7.82483727290871
    },
]