This project focuses on predicting the quality of wine based on its physicochemical properties such as acidity, sugar content, pH, alcohol, and more. The dataset used comes from the UCI Machine Learning Repository. The goal is to build a model that can classify wines as high or low quality using these numerical features.

A Random Forest Classifier was used as the main machine learning algorithm because of its ability to handle non-linear relationships and reduce overfitting. The notebook includes key steps such as exploratory data analysis (EDA), feature selection, data preprocessing, model training, hyperparameter tuning, and performance evaluation using accuracy, confusion matrix, and ROC-AUC score.