https://github.com/laavanjan/pytorch-tabular-classification
This project demonstrates how to build a binary classification model for tabular data using PyTorch.
https://github.com/laavanjan/pytorch-tabular-classification
binary-classification deep-learning kaggle-dataset pandas pytorch sklearn
Last synced: 2 months ago
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This project demonstrates how to build a binary classification model for tabular data using PyTorch.
- Host: GitHub
- URL: https://github.com/laavanjan/pytorch-tabular-classification
- Owner: laavanjan
- License: gpl-3.0
- Created: 2025-07-25T18:28:23.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-07-25T18:30:26.000Z (2 months ago)
- Last Synced: 2025-07-26T01:16:28.338Z (2 months ago)
- Topics: binary-classification, deep-learning, kaggle-dataset, pandas, pytorch, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 116 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Tabular Data Classification with PyTorch
This project demonstrates how to build a binary classification model for tabular data using PyTorch. The workflow is implemented in the notebook `Tabular_Data_Classification.ipynb` and uses a rice type classification dataset from Kaggle.
## Features
- **Dataset Download:** Automatically downloads the rice type classification dataset using `opendatasets`.
- **Data Preprocessing:** Cleans the data by removing missing values and normalizes features for better model performance.
- **Data Splitting:** Splits the data into training, validation, and test sets.
- **PyTorch Dataset & DataLoader:** Wraps the data in custom PyTorch Dataset objects and uses DataLoader for efficient batching.
- **Model Definition:** Implements a simple feedforward neural network for binary classification.
- **Training & Validation:** Trains the model and tracks loss and accuracy for both training and validation sets.
- **Testing:** Evaluates the model on the test set and reports accuracy.
- **Visualization:** Plots training/validation loss and accuracy over epochs.
- **Inference:** Allows user input for feature values to make predictions with the trained model.
## Usage
1. Open `Tabular_Data_Classification.ipynb` in Jupyter or VS Code.
2. Run all cells sequentially to:
- Download and preprocess the dataset
- Train and evaluate the model
- Visualize results
- Make predictions using custom input
## Requirements
- Python 3.7+
- PyTorch
- scikit-learn
- matplotlib
- pandas
- numpy
- opendatasets
- torchsummary
Install dependencies with:
```python
!pip install opendatasets torch torchsummary scikit-learn matplotlib pandas numpy
```
## Dataset
- [Rice Type Classification Dataset](https://www.kaggle.com/datasets/mssmartypants/rice-type-classification)
## Notes
- The notebook is designed for educational purposes and can be adapted for other tabular classification tasks.
- GPU acceleration is supported if available.
## License
This project is provided for educational use. Please check the dataset license before using for commercial purposes.