Fitting t-SNE to the ANSUR data

t-SNE is a great technique for visual exploration of high dimensional datasets. In this exercise, you'll apply it to the ANSUR dataset. You'll remove non-numeric columns from the pre-loaded dataset df and fit TSNE to this numeric dataset.

This exercise is part of the course

Dimensionality Reduction in Python

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Exercise instructions

Drop the non-numeric columns from the dataset.
Create a TSNE model with learning rate 50.
Fit and transform the model on the numeric dataset.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Non-numerical columns in the dataset
non_numeric = ['Branch', 'Gender', 'Component']

# Drop the non-numerical columns from df
df_numeric = df.____(____, axis=____)

# Create a t-SNE model with learning rate 50
m = ____(____)

# Fit and transform the t-SNE model on the numeric dataset
tsne_features = m.____(____)
print(tsne_features.shape)

Edit and Run Code