Water Quality Prediction Model

This project aims to predict water quality based on various indicators such as pH levels, turbidity, temperature, and dissolved oxygen. The model is built using TensorFlow and Keras.

Project Structure

• model.ipynb: Jupyter notebook containing the model definition, training, and prediction code.
• data/: Directory containing sample data files (if any).
• env/: Virtual environment directory.
• README.md: Project documentation.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/water-quality-prediction.git
   cd water-quality-prediction

2. Create and activate a virtual environment:

   python -m venv env
   .\env\Scripts\activate  # On Windows
   source env/bin/activate  # On macOS/Linux

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Open the Jupyter notebook:

   jupyter notebook model.ipynb

2. Run the cells to train the model and make predictions.

Sample Data

The sample data for water quality indicators is generated using the following code:

import numpy as np

# Sample data for water quality indicators
pH_levels = np.random.uniform(6.5, 8.5, 70)  # pH levels between 6.5 and 8.5
turbidity = np.random.uniform(0, 5, 70)  # Turbidity in NTU (0 to 5)
temperature = np.random.uniform(10, 30, 70)  # Temperature in Celsius (10 to 30)
dissolved_oxygen = np.random.uniform(5, 14, 70)  # Dissolved oxygen in mg/L (5 to 14)

# Combine the features into a single dataset
X_data = np.column_stack((pH_levels, turbidity, temperature, dissolved_oxygen))

# Example labels (1 for positive, 0 for negative)
Y_data = np.random.randint(0, 2, 70)


License
This project is licensed under the MIT License. See the LICENSE file for details.