Get startedGet started for free

A t-SNE map of the stock market

t-SNE provides great visualizations when the individual samples can be labeled. In this exercise, you'll apply t-SNE to the company stock price data. A scatter plot of the resulting t-SNE features, labeled by the company names, gives you a map of the stock market! The stock price movements for each company are available as the array normalized_movements (these have already been normalized for you). The list companies gives the name of each company. PyPlot (plt) has been imported for you.

This exercise is part of the course

Unsupervised Learning in Python

View Course

Exercise instructions

  • Import TSNE from sklearn.manifold.
  • Create a TSNE instance called model with learning_rate=50.
  • Apply the .fit_transform() method of model to normalized_movements. Assign the result to tsne_features.
  • Select column 0 and column 1 of tsne_features.
  • Make a scatter plot of the t-SNE features xs and ys. Specify the additional keyword argument alpha=0.5.
  • Code to label each point with its company name has been written for you using plt.annotate(), so just hit submit to see the visualization!

Hands-on interactive exercise

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

# Import TSNE
____

# Create a TSNE instance: model
model = ____

# Apply fit_transform to normalized_movements: tsne_features
tsne_features = ____

# Select the 0th feature: xs
xs = ____

# Select the 1th feature: ys
ys = tsne_features[:,1]

# Scatter plot
____

# Annotate the points
for x, y, company in zip(xs, ys, companies):
    plt.annotate(company, (x, y), fontsize=5, alpha=0.75)
plt.show()
Edit and Run Code