Our goal is to discover what’s the quality of a wine considering only its chemical properties.

Our dataset is downloaded from UCI open repository and is composed by the following features:

• fixed acidity
• volatile acidity
• citric acid
• residual sugar
• chlorides
• free sulfur dioxide
• total sulfur dioxide
• density
• pH
• sulphates
• alcohol
• quality

Implemented Random Forest classifier(with n_estimators = 100) to classify wine as bad, average or good here referred as 1, 2, and 3 respectively. We have used Grid Search cross-validation for tuning hyperparameters. We have also used PCA to get rid of unnecessary features. Metrics used are - precision, recall, f1 score, and accuracy. At last, we dump the model to store it for future use.