Quali titoli si muovono insieme?

Nel precedente esercizio, hai raggruppato le aziende in base ai movimenti giornalieri dei prezzi azionari. Quindi, quali aziende hanno prezzi che tendono a cambiare nello stesso modo? Ora esaminerai le etichette dei cluster dal tuo clustering per scoprirlo.

La tua soluzione all'esercizio precedente è già stata eseguita. Ricorda che hai costruito una Pipeline pipeline contenente un modello KMeans e l'hai addestrata sull'array NumPy movements dei movimenti giornalieri dei titoli. Inoltre, è disponibile una lista companies con i nomi delle aziende.

Questo esercizio fa parte del corso

Apprendimento non supervisionato in Python

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Istruzioni dell'esercizio

Importa pandas come pd.
Usa il metodo .predict() della pipeline per prevedere le etichette per movements.
Allinea le etichette dei cluster con la lista dei nomi delle aziende companies creando un DataFrame df con labels e companies come colonne. Questo è già stato fatto per te.
Usa il metodo .sort_values() di df per ordinare il DataFrame in base alla colonna 'labels' e stampa il risultato.
Premi Invia e prenditi un momento per vedere quali aziende compaiono insieme in ciascun cluster!

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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(____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Apprendimento non supervisionato in Python

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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: Quanti cluster?Exercise 3: Clustering di punti 2D Exercise 4: Esamina il tuo clustering Exercise 5: Valutare un clustering Exercise 6: Quanti cluster di cereali?Exercise 7: Valutare il clustering dei cereali Exercise 8: Trasformare le caratteristiche per cluster più efficaci Exercise 9: Scalare i dati sui pesci per il clustering Exercise 10: Clustering dei dati sui pesci Exercise 11: Clustering dei titoli azionari con KMeans Exercise 12: Quali titoli si muovono insieme?

Esercizio in corso

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