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The model is trained on wine characteristics and aims to classify wines based on their quality score.\n\n📋 Features\n  Machine Learning Model: Implements Support Vector Machine (SVM) for classification.\n  Wine Quality Prediction: Predicts wine quality based on physicochemical attributes such as acidity, alcohol, and sugar levels.\n  Data Processing: Data is preprocessed and normalized for better model performance.\n  Visualization: Includes data visualization to understand feature importance and distribution.\n  \n🛠️ Technologies Used\n  Python\n    Scikit-Learn – for building and training the SVM model\n    Pandas – for data manipulation\n    NumPy – for numerical operations\n    \n📂 Project Structure\n  Wine Quality Prediction Support Vector Machine.ipynb – Main Jupyter notebook with the entire workflow.\n\n📈 Future Improvements\n  Test with different ML algorithms such as Random Forest and XGBoost.\n  Perform hyperparameter tuning to improve accuracy.\n  Deploy the model using Flask or Streamlit for real-time predictions.\n  \nContributions are welcome! 🍇\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnikhilfuke1%2Fwine-quality-prediction-support-vector-machine-python-projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnikhilfuke1%2Fwine-quality-prediction-support-vector-machine-python-projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnikhilfuke1%2Fwine-quality-prediction-support-vector-machine-python-projects/lists"}