| # Pazham 🎯 | |
| A machine learning model that predicts multiple features of a banana based on its physical characteristics: | |
| 1. Number of seeds | |
| 2. Curvature (in degrees) | |
| ## Basic Details | |
| ### Team Name: (AB)² | |
| ### Team Members | |
| - Team Lead: Atul Biju - Adi Shankara Institute of Engineering and Technology | |
| - Member 2: Amal Babu - Adi Shankara Institute of Engineering and Technology | |
| ## Overview | |
| This project uses a Random Forest Regressor to predict multiple banana characteristics based on various physical features. The model achieves good accuracy (R² scores > 0.80) on synthetic data and can be retrained with real-world data. | |
| ## Features | |
| ### Input Features | |
| The model takes the following measurements as input: | |
| - Length (centimeters) | |
| - Width (centimeters) | |
| - Weight (grams) | |
| - Ripeness level (scale 1-5) | |
| - Color (1=green, 2=yellow, 3=brown) | |
| ### Predictions | |
| The model predicts: | |
| 1. Number of seeds | |
| 2. Curvature (degrees) | |
| ## Requirements | |
| - Python 3.x | |
| - Required packages: | |
| - numpy | |
| - pandas | |
| - scikit-learn | |
| ## Usage | |
| The model is implemented in a Jupyter notebook (`model.ipynb`). To use it: | |
| 1. Open `model.ipynb` in Jupyter or VS Code | |
| 2. Run all cells to train the model | |
| 3. Use the `predict_seeds()` function with your banana measurements | |
| Example usage: | |
| ```python | |
| predictions = predict_banana_features( | |
| length=16, # cm | |
| width=3.2, # cm | |
| weight=130, # g | |
| ripeness=4, # scale 1-5 | |
| color=2 # yellow | |
| ) | |
| print(f"Predicted seeds: {predictions['seeds']}") | |
| print(f"Predicted curvature: {predictions['curvature']}°") | |
| ``` | |
| ## Model Performance | |
| Current model metrics on synthetic data: | |
| - Mean Squared Error: 0.20 | |
| - R² Score: 0.80 | |
| Note: These metrics are based on synthetic training data. Performance may vary with real-world data. | |
| ## Future Improvements | |
| - Replace synthetic data with real banana measurements | |
| - Add image processing to automatically extract features | |
| - Implement cross-validation | |
| - Add visualization of feature importance | |
| - Create a simple web interface for predictions | |
| ## License | |
| [MIT License](LICENSE) | |