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Classifying wine instances into multiple classes based on their features.

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K-Nearest Neighbors (KNN) wine Classification Project

This project implements the K-Nearest Neighbors (KNN) classification algorithm using Python and scikit-learn library. The goal is to classify instances of wine into multiple classes based on their features.

Project Highlights

  • Utilized a dataset with multiple classes and features
  • Split the data into training and test sets for model evaluation
  • Built a pipeline to scale the features and apply the KNN algorithm
  • Explored cross-validation to find the optimal K value
  • Evaluated the model's performance using accuracy scores, misclassification error, and a confusion matrix
  • Visualized the results using line plots and a confusion matrix table

Results

The project demonstrates the implementation and evaluation of the KNN classification algorithm. It showcases how to find the optimal K value using cross-validation, assess model performance with various metrics, and visualize the results through plots and tables.

Technologies Used

  • Python
  • scikit-learn
  • pandas
  • matplotlib
  • seaborn

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Classifying wine instances into multiple classes based on their features.

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