pazham / README.md
Icarus013's picture
Upload folder using huggingface_hub
bc8984b verified
# 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)