<｜begin▁of▁sentence｜>### Instruction:
Certainly! Imagine you're working remotely for a data analysis firm, and your client has provided you with a robust dataset containing 100 records, each showcasing 50 distinct attributes. The client's goal is to visualize the complex relationships within this data more simply. To fulfill this request, you are considering employing the `ClassicalMDS` method available in the `graspologic` Python module. The challenge is to effectively condense the data into a 2D representation that faithfully retains the original multidimensional distances between the data points. How would you approach using `ClassicalMDS` to achieve dimensionality reduction while preserving the relational integrity of the dataset in this two-dimensional plot?
### Response:
from graspologic.embed import ClassicalMDS
import numpy as np

# Generate synthetic dataset
X = np.random.rand(100, 50)  # 100 samples with 50 features each

# Instantiate and fit the ClassicalMDS
model = ClassicalMDS(n_components=2, dissimilarity='euclidean')
X_transformed = model.fit_transform(X)

# X_transformed now contains the 2-dimensional embedding of the original dataset
print(X_transformed)
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