# Import libraries
import matplotlib.pyplot as plt
from sklearn.datasets import load_wine
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

# Load dataset
wine = load_wine()
X = wine.data   # only features (unlabeled data)

# Standardize features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)


# Apply PCA (reduce to 2 dimensions)
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)

print("Explained variance ratio:", pca.explained_variance_ratio_)
print("Total variance captured:", sum(pca.explained_variance_ratio_))

plt.figure(figsize=(8, 6))
plt.scatter(X_pca[:, 0], X_pca[:, 1], s=50)
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")
plt.title("PCA: Data Reduced from 13D to 2D")
plt.grid(True)
plt.show()

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