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:

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