https://github.com/nikhilfuke1/wine-quality-prediction-support-vector-machine-python-projects

This project focuses on predicting the quality of wine using a Support Vector Machine (SVM) model. The model is trained on wine characteristics and aims to classify wines based on their quality score.
https://github.com/nikhilfuke1/wine-quality-prediction-support-vector-machine-python-projects

numpy-library pandas-library python sklearn-library svm-model

Last synced: 27 days ago
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This project focuses on predicting the quality of wine using a Support Vector Machine (SVM) model. The model is trained on wine characteristics and aims to classify wines based on their quality score.

• Host: GitHub
• URL: https://github.com/nikhilfuke1/wine-quality-prediction-support-vector-machine-python-projects
• Owner: nikhilfuke1
• Created: 2025-01-03T09:43:40.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-21T03:17:58.000Z (about 1 year ago)
• Last Synced: 2025-07-27T20:59:55.824Z (11 months ago)
• Topics: numpy-library, pandas-library, python, sklearn-library, svm-model
• Language: Jupyter Notebook
• Homepage:
• Size: 105 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Project 1 - Wine Quality Prediction Support Vector Machine

Utilized Python, pandas, and scikit-learn to build and optimize the predictive model.

🍷 Wine Quality Prediction - SVM Model

  This project focuses on predicting the quality of wine using a Support Vector Machine (SVM) model. The model is trained on wine characteristics and aims to classify wines based on their quality score.

📋 Features

  Machine Learning Model: Implements Support Vector Machine (SVM) for classification.

  Wine Quality Prediction: Predicts wine quality based on physicochemical attributes such as acidity, alcohol, and sugar levels.

  Data Processing: Data is preprocessed and normalized for better model performance.

  Visualization: Includes data visualization to understand feature importance and distribution.

  

🛠️ Technologies Used

  Python

    Scikit-Learn – for building and training the SVM model

    Pandas – for data manipulation

    NumPy – for numerical operations

    

📂 Project Structure

  Wine Quality Prediction Support Vector Machine.ipynb – Main Jupyter notebook with the entire workflow.

📈 Future Improvements

  Test with different ML algorithms such as Random Forest and XGBoost.

  Perform hyperparameter tuning to improve accuracy.

  Deploy the model using Flask or Streamlit for real-time predictions.

  

Contributions are welcome! 🍇