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🧠 WasteClassificationCNN

An AICTE Virtual Internship (Cycle 3) project focused on waste classification using Convolutional Neural Networks (CNN).
This project helps identify and categorize waste into different types using image classification techniques, contributing towards environmental sustainability.

📁 Dataset

The dataset used in this project is available on Kaggle.

You can also download it programmatically using:

import kagglehub  # You may need to run: pip install kagglehub

path = kagglehub.dataset_download("techsash/waste-classification-data")
print("Path to dataset files:", path)

🛠️ Technologies Used

  • Python 🐍
  • TensorFlow / Keras
  • NumPy, Pandas
  • Matplotlib, Seaborn
  • Jupyter Notebook

🧠 Model Overview

We use a CNN (Convolutional Neural Network) to classify waste images into categories like:

  • Organic
  • Recyclable

The model is trained on labeled images and evaluated using accuracy, loss, and confusion matrix.

📷 Screenshots

📌 Dataset Sample

Dataset Sample

🧠 Model Interface

Interface

📈 Training Results

Training Results

🚀 Getting Started

  1. Clone the Repository

    git clone https://github.com/Shrutik1008/WasteClassificationUsingCNN.git
cd WasteClassificationUsingCNN

  2. Install Dependencies

    pip install -r requirements.txt

  3. Download Dataset Use the Kaggle link above or download via kagglehub.

  4. Run the Project Launch the notebook or script to start training:

    jupyter notebook WasteClassificationCNN.ipynb

📌 Future Improvements

  • Improve classification accuracy with deeper architectures.
  • Add data augmentation and transfer learning.
  • Deploy model with a user interface using Streamlit or Flask.

🙏 Acknowledgements