In this project, I tackled a classification problem using wine data , aiming to group the wines into three distinct classes. To achieve this, I employed various classification models, including Logistic Regression, KNeighborsClassifier, XGBoost, RandomForest, among others. Additionally, I applied data preprocessing techniques such as feature scaling and Linear Discriminant Analysis (LDA) to ensure efficient model training and enhance predictive accuracy.
