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.
- 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
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.
- Python
- scikit-learn
- pandas
- matplotlib
- seaborn