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Icarus013/
pazham

Name: pazham
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Pazham 🎯

A machine learning model that predicts multiple features of a banana based on its physical characteristics:

Number of seeds
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:

Number of seeds
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:

Open model.ipynb in Jupyter or VS Code
Run all cells to train the model
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

Icarus013/pazham