Mengelompokkan saham menggunakan KMeans

Dalam latihan ini, Anda akan mengelompokkan perusahaan berdasarkan pergerakan harga saham harian mereka (yaitu selisih dolar antara harga penutupan dan pembukaan untuk setiap hari perdagangan). Anda diberikan array NumPy movements berisi pergerakan harga harian dari 2010 hingga 2015 (diperoleh dari Yahoo! Finance), di mana setiap baris merepresentasikan sebuah perusahaan, dan setiap kolom merepresentasikan satu hari perdagangan.

Beberapa saham lebih mahal daripada yang lain. Untuk mengatasinya, sertakan Normalizer di awal pipeline Anda. Normalizer akan secara terpisah mentransformasikan harga saham setiap perusahaan ke skala relatif sebelum proses pengelompokan dimulai.

Perhatikan bahwa Normalizer() berbeda dengan StandardScaler(), yang Anda gunakan pada latihan sebelumnya. Jika StandardScaler() menstandarkan fitur (seperti fitur pada data ikan di latihan sebelumnya) dengan menghilangkan rata-rata dan menskalakan ke varians satuan, Normalizer() menskalakan ulang setiap sampel - di sini, harga saham tiap perusahaan - secara independen dari yang lain.

KMeans dan make_pipeline sudah diimpor untuk Anda.

Latihan ini adalah bagian dari kursus

Unsupervised Learning in Python

Petunjuk latihan

Impor Normalizer dari sklearn.preprocessing.
Buat sebuah instans Normalizer bernama normalizer.
Buat sebuah instans KMeans bernama kmeans dengan 10 klaster.
Dengan menggunakan make_pipeline(), buat sebuah pipeline bernama pipeline yang merangkai normalizer dan kmeans.
Latih pipeline pada array movements.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import Normalizer
____

# Create a normalizer: normalizer
normalizer = ____

# Create a KMeans model with 10 clusters: kmeans
kmeans = ____

# Make a pipeline chaining normalizer and kmeans: pipeline
pipeline = ____

# Fit pipeline to the daily price movements
____

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: Ada berapa klaster?Exercise 3: Melakukan klasterisasi titik 2D Exercise 4: Periksa klastering Anda Exercise 5: Mengevaluasi sebuah pengelompokan Exercise 6: Berapa banyak klaster untuk biji-bijian?Exercise 7: Mengevaluasi pengelompokan biji-bijian Exercise 8: Mentransformasi fitur untuk pengelompokan yang lebih baik Exercise 9: Menskalakan data ikan untuk klastering Exercise 10: Mengelompokkan data ikan Exercise 11: Mengelompokkan saham menggunakan KMeans

Latihan Saat Ini

Exercise 12: Saham mana yang bergerak bersama?

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