X = data.drop("quality", axis=1)
y = data["quality"]

X_scaled = StandardScaler().fit_transform(X)

pca = PCA().fit(X_scaled)
pca_res = pca.transform(X_scaled)

pca_res_df = pd.DataFrame(pca_res, columns=[f"PC{i}" for i in range(1, pca_res.shape[1] + 1)])
pca_res_df.head()