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! 🍇