Wine Quality Prediction Using K-Nearest Neighbors (KNN) Classifier

This project demonstrates the implementation of the K-Nearest Neighbors (KNN) algorithm to predict wine quality based on various physicochemical properties using the Wine Quality dataset.

Overview

The notebook covers the following steps:

• Reading and preprocessing the Wine Quality dataset.
• Splitting the data into training and testing sets.
• Standardizing the features using StandardScaler.
• Training a KNN model with default parameters.
• Evaluating model performance using accuracy, confusion matrix, and classification report.
• Hyperparameter tuning using GridSearchCV to find the best number of neighbors for KNN.
• Visualizing the confusion matrix of the best model.

Requirements

• Python 3.x
• Pandas
• Scikit-learn
• Seaborn
• Matplotlib

How to Run

1. Install the required libraries using:

   pip install pandas scikit-learn seaborn matplotlib