Welke aandelen bewegen samen?

In de vorige oefening heb je bedrijven geclusterd op basis van hun dagelijkse koersbewegingen. Welke bedrijven veranderen hun koers op een vergelijkbare manier? Je gaat nu de clusterlabels van je clustering bekijken om daarachter te komen.

Je oplossing van de vorige oefening is al uitgevoerd. Ter herinnering: je hebt een Pipeline pipeline gebouwd met een KMeans-model en die gefit op de NumPy-array movements met dagelijkse koersbewegingen. Daarnaast is er een lijst companies met de bedrijfsnamen beschikbaar.

Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

Cursus bekijken

Oefeninstructies

Importeer pandas als pd.
Gebruik de .predict()-methode van de pipeline om de labels voor movements te voorspellen.
Lijn de clusterlabels uit met de lijst met bedrijfsnamen companies door een DataFrame df te maken met labels en companies als kolommen. Dit is al voor je gedaan.
Gebruik de .sort_values()-methode van df om de DataFrame te sorteren op de kolom 'labels', en print het resultaat.
Klik op Antwoord verzenden en neem even de tijd om te zien welke bedrijven in elke cluster bij elkaar staan!

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import pandas
import pandas as pd

# Predict the cluster labels: labels
labels = ____

# Create a DataFrame aligning labels and companies: df
df = pd.DataFrame({'labels': labels, 'companies': companies})

# Display df sorted by cluster label
print(____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.

Exercise 1: Unsupervised Learning Exercise 2: Hoeveel clusters?Exercise 3: Clusteren van 2D-punten Exercise 4: Bekijk je clustering Exercise 5: Een clustering evalueren Exercise 6: Hoeveel graanclusters?Exercise 7: De graanclustering evalueren Exercise 8: Features transformeren voor betere clusterings Exercise 9: Vissengegevens schalen voor clustering Exercise 10: Clusteren van de visgegevens Exercise 11: Aandelen clusteren met KMeans Exercise 12: Welke aandelen bewegen samen?

Huidige oefening

In this chapter, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE. Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy. t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.

Exercise 1: Visualizing hierarchies Exercise 2: How many merges?Exercise 3: Hierarchical clustering of the grain data Exercise 4: Hierarchies of stocks Exercise 5: Cluster labels in hierarchical clustering Exercise 6: Which clusters are closest?Exercise 7: Different linkage, different hierarchical clustering!Exercise 8: Intermediate clusterings Exercise 9: Extracting the cluster labels Exercise 10: t-SNE for 2-dimensional maps Exercise 11: t-SNE visualization of grain dataset Exercise 12: A t-SNE map of the stock market

Dimension reduction summarizes a dataset using its common occuring patterns. In this chapter, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning. For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!

Exercise 1: Visualizing the PCA transformation Exercise 2: Correlated data in nature Exercise 3: Decorrelating the grain measurements with PCA Exercise 4: Principal components Exercise 5: Intrinsic dimension Exercise 6: The first principal component Exercise 7: Variance of the PCA features Exercise 8: Intrinsic dimension of the fish data Exercise 9: Dimension reduction with PCA Exercise 10: Dimension reduction of the fish measurements Exercise 11: A tf-idf word-frequency array Exercise 12: Clustering Wikipedia part I Exercise 13: Clustering Wikipedia part II

In this chapter, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts. For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns. You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!

Exercise 1: Non-negative matrix factorization (NMF)Exercise 2: Non-negative data Exercise 3: NMF applied to Wikipedia articles Exercise 4: NMF features of the Wikipedia articles Exercise 5: NMF reconstructs samples Exercise 6: NMF learns interpretable parts Exercise 7: NMF learns topics of documents Exercise 8: Explore the LED digits dataset Exercise 9: NMF learns the parts of images Exercise 10: PCA doesn't learn parts Exercise 11: Building recommender systems using NMF Exercise 12: Which articles are similar to 'Cristiano Ronaldo'?Exercise 13: Recommend musical artists part I Exercise 14: Recommend musical artists part II Exercise 15: Final thoughts