https://github.com/djdurga/wine-quality-prediction-using-svm
This project demonstrates a hands-on implementation of Support Vector Machine (SVM) for a binary classification task using Python and Scikit-learn.
https://github.com/djdurga/wine-quality-prediction-using-svm
Last synced: about 1 year ago
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This project demonstrates a hands-on implementation of Support Vector Machine (SVM) for a binary classification task using Python and Scikit-learn.
- Host: GitHub
- URL: https://github.com/djdurga/wine-quality-prediction-using-svm
- Owner: Djdurga
- Created: 2025-06-17T17:10:07.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-17T17:11:10.000Z (about 1 year ago)
- Last Synced: 2025-06-17T18:24:22.937Z (about 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 32.2 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🍷 Wine Quality Prediction using SVM
This notebook demonstrates the use of **Support Vector Machine (SVM)** to classify wine quality based on physicochemical features using the `WineQT.csv` dataset.
---
## 📌 Overview
We aim to predict the **quality of wine** (likely framed as a classification problem) using the Support Vector Machine (SVM) algorithm. The workflow includes:
- Data loading and exploration
- Data preprocessing
- Model training using SVM
- Performance evaluation using metrics like accuracy, precision, recall, F1-score
- Visualization using seaborn/matplotlib
---
## 🔧 Technologies Used
- Python
- pandas, numpy
- matplotlib, seaborn
- scikit-learn (for SVM and evaluation)
---
## 📁 Dataset
The dataset used is `WineQT.csv`, which contains various chemical properties of red wine and a quality score.
Example features:
- Fixed Acidity
- Volatile Acidity
- Citric Acid
- Residual Sugar
- pH
- Alcohol
- Quality (target variable)
---
## ⚙️ How to Run
1. Clone this repository:
```bash
git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name