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Researchers studying chemical properties of wines collected data on a sample of white wines in Northern Portugal. A research goal was to cluster wines based

Researchers studying chemical properties of wines collected data on a sample of white wines in Northern Portugal. A research goal was to cluster wines based on similar chemical properties.
Using pdist(), calculate a distance matrix for wines. The matrix of input features, X, has already been created.
Use the distance matrix to cluster the wines with centroid linkage.
The code provided creates a dataframe with two features (chlorides and alcohol), normalizes the dataframe, and displays the cluster membership of each data point.
import pandas as pd
from scipy.cluster.hierarchy import linkage
from scipy.spatial.distance import pdist
from sklearn.preprocessing import StandardScaler
wine = pd.read_csv('wine1.csv')
# Calculate a distance matrix with selected variables
X = wine[['chlorides', 'alcohol']]
scaler = StandardScaler()
X = pd.DataFrame(scaler.fit_transform(X))
# Your code goes here
print(clusterModel)

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