# 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)