https://github.com/haekalsetiawan/banana-quality-ml-analysis

The Banana Quality Analysis ML project classifies the quality of bananas using machine learning. By analyzing banana images and relevant features, the model categorizes bananas into quality tiers. Key stages include data preprocessing, feature extraction, model training, and evaluation, resulting in high accuracy.
https://github.com/haekalsetiawan/banana-quality-ml-analysis

pandas python scikit-learn

Last synced: 8 days ago
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The Banana Quality Analysis ML project classifies the quality of bananas using machine learning. By analyzing banana images and relevant features, the model categorizes bananas into quality tiers. Key stages include data preprocessing, feature extraction, model training, and evaluation, resulting in high accuracy.

• Host: GitHub
• URL: https://github.com/haekalsetiawan/banana-quality-ml-analysis
• Owner: haekalsetiawan
• License: mit
• Created: 2024-10-20T13:35:34.000Z (19 days ago)
• Default Branch: main
• Last Pushed: 2024-10-20T19:11:13.000Z (18 days ago)
• Last Synced: 2024-10-20T23:44:52.758Z (18 days ago)
• Topics: pandas, python, scikit-learn
• Language: Python
• Homepage:
• Size: 338 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.MD
  • License: LICENSE

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README

        
# Banana Quality Analysis

This project is a machine learning-based analysis for predicting the quality of bananas. Using features such as size, weight, sweetness, softness, and more, the model predicts whether a banana is of "Good" or "Bad" quality.

## Project Workflow

1. **Data Preprocessing**: Data is cleaned, normalized, and split into features and target.

2. **Model Training**: A Random Forest Classifier is trained on the processed data.

3. **Evaluation**: The model is evaluated based on accuracy and precision using metrics like classification report.

## Setup and Usage

1. Clone this repository:

git clone https://github.com/USERNAME/banana_quality_analysis.git

2. Navigate to the project directory:

cd banana_quality_analysis

3. Install the required Python packages:

pip install -r requirements.txt

4. Run the project:

python main.py

## Results

The model achieves an accuracy of 97% in predicting banana quality, with strong performance in both "Good" and "Bad" categories.

## Contributors

Haekal Setiawan (@haekalsetiawan)

## License

This project is licensed under the MIT License - see the LICENSE file for details.