Wine Quality Prediction

Overview~

This project leverages machine learning to predict the quality of wine based on its physicochemical properties. The objective is to develop a robust classification model that aids in determining wine quality, offering value to vintners, distributors, and consumers alike.

Tools~

Python, NumPy, pandas, scikit-learn, matplotlib, seaborn

Approach~

• Data Preprocessing: Cleaned and standardized data for model training.

• EDA: Visualized relationships between chemical features and wine quality.

• Modeling: Tested RandomForestClassifier model.

• Evaluation: Assessed models using accuracy_score.

Results~

• Model: RandomForestClassifier model with 90% accuracy

• Insights: Key factors like alcohol and acidity affect wine quality.