🍷 Wine Quality Analysis

This project performs an in-depth analysis and prediction of wine quality using machine learning techniques. It explores various data preprocessing, visualization, and classification approaches to determine factors that impact wine quality.

📁 Project Structure

📊 Dataset

• Source: UCI Machine Learning Repository
• Features: 11 physicochemical attributes (like acidity, pH, alcohol, etc.)
• Target: Quality score (0-10)

🔍 Exploratory Data Analysis

• Null value detection
• Distribution plots for all features
• Correlation heatmap
• Feature importance analysis

🤖 Machine Learning Models Used

• Logistic Regression
• Decision Tree Classifier
• Random Forest Classifier
• Support Vector Machine (SVM)
• Gradient Boosting

Each model was evaluated using accuracy, confusion matrix, and classification report.

📈 Model Evaluation

Performance metrics like:

• Accuracy
• Precision, Recall, F1-score
• Confusion matrix
• ROC Curve (if implemented)

🧪 Results

• Feature(s) like alcohol, volatile acidity, and sulphates showed strong correlation with wine quality.
• The best-performing model (e.g., Random Forest) achieved an accuracy of ~X% (fill this based on your results).