Physicochemical properties and quality ratings for wines pulled from UCI dataset.
This project we: Load the wine quality dataset from the UCI Machine Learning Repository. Explored the data by displaying basic information and statistics. Visualized feature correlations using a heatmap. Added a new categorical target column 'quality_label' based on the numeric 'quality' column. Selected features and target variables. Split the data into training and testing sets. Scaled the feature values using StandardScaler. Trains a random forest classifier. Makes predictions using the test set and evaluates the model using classification report, confusion matrix, and accuracy score.