'StandardScaler' object has no attribute 'inertia_'

`StandardScaler` is a preprocessing object in scikit-learn which standardizes features by removing the mean and scaling to unit variance. It does not have an `inertia_` attribute.

`inertia_` is an attribute of clustering algorithms like KMeans in scikit-learn, which measures the sum of squared distances of samples to their closest cluster center.

If you are trying to calculate `inertia_` after applying `StandardScaler`, you should first fit and transform your data using `StandardScaler`, then apply a clustering algorithm like KMeans and access its `inertia_` attribute. Here's an example code snippet:

from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans

# Load data
X = ...

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

# Apply KMeans clustering
kmeans = KMeans(n_clusters=3)
kmeans.fit(X_scaled)

# Access inertia_
inertia = kmeans.inertia_