https://github.com/gokulnpc/wine-quality-prediction
This web app is created to predict the quality of red wine based on the input features. The model is trained using the Random Forest Classifier algorithm.
https://github.com/gokulnpc/wine-quality-prediction
classification-model machine-learning random-forest-classifier stream
Last synced: about 2 months ago
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This web app is created to predict the quality of red wine based on the input features. The model is trained using the Random Forest Classifier algorithm.
- Host: GitHub
- URL: https://github.com/gokulnpc/wine-quality-prediction
- Owner: gokulnpc
- Created: 2024-03-21T06:15:27.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-21T07:25:00.000Z (about 1 year ago)
- Last Synced: 2025-02-02T03:44:51.951Z (4 months ago)
- Topics: classification-model, machine-learning, random-forest-classifier, stream
- Language: Jupyter Notebook
- Homepage: https://wine-quality-prediction-gokulnpc.streamlit.app/
- Size: 471 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Wine Quality Prediction
This web app is created to predict the quality of red wine based on the input features.
The model is trained using the Random Forest Classifier algorithm.
The dataset consists of 11 input features and 1 output feature. The input features are:
1. fixed acidity
2. volatile acidity
3. citric acid
4. residual sugar
5. chlorides
6. free sulfur dioxide
7. total sulfur dioxide
8. density
9. pH
10. sulphates
11. alcohol
The output feature is the quality of the wine, which is a binary variable (0 or 1).
