Hierarki saham

Pada bab 1, Anda menggunakan k-means clustering untuk mengelompokkan perusahaan berdasarkan pergerakan harga sahamnya. Sekarang, Anda akan melakukan hierarchical clustering pada perusahaan-perusahaan tersebut. Anda diberikan sebuah array NumPy berisi pergerakan harga movements, di mana setiap baris mewakili perusahaan, serta sebuah daftar nama perusahaan companies. Hierarchical clustering dari SciPy tidak dapat dimasukkan ke dalam pipeline sklearn, sehingga Anda perlu menggunakan fungsi normalize() dari sklearn.preprocessing alih-alih Normalizer.

linkage dan dendrogram sudah diimpor dari scipy.cluster.hierarchy, dan PyPlot telah diimpor sebagai plt.

Latihan ini adalah bagian dari kursus

Unsupervised Learning in Python

Petunjuk latihan

Impor normalize dari sklearn.preprocessing.
Skala ulang pergerakan harga untuk setiap saham dengan menggunakan fungsi normalize() pada movements.
Terapkan fungsi linkage() pada normalized_movements, menggunakan linkage 'complete', untuk menghitung hierarchical clustering. Simpan hasilnya ke mergings.
Plot dendrogram dari hierarchical clustering, menggunakan daftar companies berisi nama-nama perusahaan sebagai labels. Selain itu, tentukan argumen kata kunci leaf_rotation=90 dan leaf_font_size=6 seperti pada latihan sebelumnya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import normalize
____

# Normalize the movements: normalized_movements
normalized_movements = ____

# Calculate the linkage: mergings
mergings = ____

# Plot the dendrogram
____
plt.show()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Unsupervised Learning in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: How many clusters?Exercise 3: Clustering 2D points Exercise 4: Inspect your clustering Exercise 5: Evaluating a clustering Exercise 6: How many clusters of grain?Exercise 7: Evaluating the grain clustering Exercise 8: Transforming features for better clusterings Exercise 9: Scaling fish data for clustering Exercise 10: Clustering the fish data Exercise 11: Clustering stocks using KMeans Exercise 12: Which stocks move together?

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: Memvisualisasikan hierarki Exercise 2: Berapa banyak penggabungan?Exercise 3: Hierarchical clustering pada data biji-bijian Exercise 4: Hierarki saham

Latihan Saat Ini

Exercise 5: Label klaster dalam hierarchical clustering Exercise 6: Klaster mana yang paling dekat?Exercise 7: Linkage berbeda, hierarchical clustering berbeda!Exercise 8: Pengelompokan menengah Exercise 9: Mengekstrak label klaster Exercise 10: t-SNE untuk peta 2 dimensi Exercise 11: Visualisasi t-SNE pada himpunan data biji-bijian Exercise 12: Peta t-SNE pasar saham

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