Skip to content

Latest commit

 

History

History
88 lines (63 loc) · 2.26 KB

README.md

File metadata and controls

88 lines (63 loc) · 2.26 KB

🌊 Water Quality Classifier using PyTorch

Python PyTorch License: MIT

A deep learning-based binary classification project to predict water quality using a 3-layer dense neural network built in PyTorch, trained on normalized water quality data.

📁 Project Structure

DL-WaterQuality-Classifier/
├── DL_WaterQuality.ipynb  	                  # Main training/testing notebook
├── water_quality.csv                             # Dataset file
├── requirements.txt                              # Python dependencies
├── README.md                                     # Project overview
└── .gitignore                                    # Git exclusion rules

📌 Features

  • Clean and preprocess water quality dataset
  • Normalize features
  • Build and train a deep neural network in PyTorch
  • Evaluate performance with accuracy and loss plots

🚀 Getting Started

1. Clone the Repository

git clone https://github.com/shahsanjanav/DL-WaterQuality-Classifier.git
cd DL-WaterQuality-Classifier

2. Install Dependencies

pip install -r requirements.txt

3. Launch the Notebook

jupyter notebook DL_WaterQuality.ipynb

🧠 Model Architecture

  • Input Layer: Based on number of features
  • Hidden Layers: Dense + ReLU + Dropout
  • Output Layer: Sigmoid activation for binary classification
  • Loss: Binary Cross Entropy Loss
  • Optimizer: Adam

📊 Dataset

The water_quality.csv dataset contains multiple physical and chemical water parameters labeled for classification.

🛠 Built With

📄 License

MIT License © 2025 Sanjana Shah

👤 Author

Sanjana Shah
✨ Machine Learning & Generative AI Enthusiast
📫 Connect on LinkedIn GitHub: @shahsanjanav

⭐ If you like this project, consider starring it on GitHub!