Tabular Data Classification using PyTorch

This project builds a deep learning classifier using PyTorch to classify rice types based on physical attributes from a structured CSV dataset.

Project Highlights

• Cleaned and normalized a rice classification dataset
• Custom PyTorch Dataset class for scalable data loading
• Deep learning model trained with train/validation/test splits
• Performance tracked using accuracy and loss metrics
• Final evaluation on unseen test data

Dataset

The dataset contains several numerical features for different rice grains, with the target being the type/class of rice.

• Columns: Numerical attributes of rice grains
• Target: Class column indicating rice type

Model Architecture

• Fully connected feedforward neural network
• Binary classification with BCEWithLogitsLoss

Results

• Real-time accuracy and loss tracking over 10 epochs
• Final test accuracy printed at the end of training

Libraries Used

• PyTorch
• Scikit-learn
• Pandas
• NumPy
• Matplotlib