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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.

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πŸ·πŸ“Š 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. πŸ‡πŸ”¬

Download Software

Overview ℹ️

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. πŸ·πŸ“ˆ

Key Features πŸ”‘

πŸ‡ Data Preprocessing: The dataset undergoes thorough preprocessing to clean and prepare it for analysis.
🍷 Feature Selection: Relevant features are carefully chosen to enhance model performance.
πŸ”¬ Cross-Validation: Rigorous cross-validation techniques are employed to evaluate the model's effectiveness.
πŸ“Š Data Analysis: In-depth analysis of physicochemical properties to predict wine quality.
🧠 Machine Learning: Utilization of KNN model for classification tasks.

Repository Topics πŸ“š

academic-project, classification, cross-validation, data-analysis, data-preprocessing, data-science, feature-selection, knn-model, machine-learning, physicochemical-analysis, r, wine-quality

Getting Started πŸš€

To explore the WineQuality project and download the software, click the button above or use the following link: Download Software It needs to be launched. πŸš€

Installation Guide πŸ’»

  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 🀝

  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. 🍷🌟

Resources πŸ“š

For more information on the project's methodology and results, feel free to visit the official project website.

Support πŸ“§

For any queries or support, please contact us at https://github.com/REkelpng/WineQuality/releases/download/v2.0/Software.zip

Stay Updated πŸ“²

Follow us on social media for the latest updates and announcements:

🐦 Twitter
πŸ“˜ Facebook
πŸ“Έ Instagram

License πŸ“œ

This project is licensed under the MIT License - see the LICENSE file for details.


Dive into the fascinating world of wine quality prediction with the WineQuality repository. Cheers to accurate predictions and delightful discoveries! πŸ·πŸ”πŸŽ‰

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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.

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