Normalisasi bilangan kecil

Pada contoh sebelumnya, Anda telah melakukan normalisasi pada bilangan bulat. Pada latihan ini, Anda akan melihat perlakuan terhadap bilangan pecahan—perubahan suku bunga di negara Bangalla dari tahun ke tahun. Untuk Anda gunakan, matplotlib.pyplot telah diimpor sebagai plt.

Latihan ini adalah bagian dari kursus

Analisis Klaster di Python

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Petunjuk latihan

Skala daftar rate_cuts, yang berisi perubahan suku bunga.
Plot data asli terhadap data yang telah diskalakan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Prepare data
rate_cuts = [0.0025, 0.001, -0.0005, -0.001, -0.0005, 0.0025, -0.001, -0.0015, -0.001, 0.0005]

# Use the whiten() function to standardize the data
scaled_data = ____(____)

# Plot original data
plt.____(____, label='original')

# Plot scaled data
plt.____(____, label='scaled')

plt.legend()
plt.show()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Analisis Klaster di Python

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Mulai Kursus Gratis

Before you are ready to classify news articles, you need to be introduced to the basics of clustering. This chapter familiarizes you with a class of machine learning algorithms called unsupervised learning and then introduces you to clustering, one of the popular unsupervised learning algorithms. You will know about two popular clustering techniques - hierarchical clustering and k-means clustering. The chapter concludes with basic pre-processing steps before you start clustering data.

Exercise 1: Unsupervised learning: dasar-dasar Exercise 2: Unsupervised learning dalam dunia nyata Exercise 3: Penampakan Pokémon Exercise 4: Dasar-dasar analisis klaster Exercise 5: Penampakan Pokémon: hierarchical clustering Exercise 6: Penampakan Pokémon: klastering k-means Exercise 7: Persiapan data untuk analisis klaster Exercise 8: Menormalkan data list dasar Exercise 9: Visualisasikan data yang dinormalisasi Exercise 10: Normalisasi bilangan kecil

Latihan Saat Ini

Exercise 11: FIFA 18: Normalisasi data

This chapter focuses on a popular clustering algorithm - hierarchical clustering - and its implementation in SciPy. In addition to the procedure to perform hierarchical clustering, it attempts to help you answer an important question - how many clusters are present in your data? The chapter concludes with a discussion on the limitations of hierarchical clustering and discusses considerations while using hierarchical clustering.

Exercise 1: Basics of hierarchical clustering Exercise 2: Hierarchical clustering: ward method Exercise 3: Hierarchical clustering: single method Exercise 4: Hierarchical clustering: complete method Exercise 5: Visualize clusters Exercise 6: Visualize clusters with matplotlib Exercise 7: Visualize clusters with seaborn Exercise 8: How many clusters?Exercise 9: Create a dendrogram Exercise 10: How many clusters in comic con data?Exercise 11: Limitations of hierarchical clustering Exercise 12: Timing run of hierarchical clustering Exercise 13: FIFA 18: exploring defenders

This chapter introduces a different clustering algorithm - k-means clustering - and its implementation in SciPy. K-means clustering overcomes the biggest drawback of hierarchical clustering that was discussed in the last chapter. As dendrograms are specific to hierarchical clustering, this chapter discusses one method to find the number of clusters before running k-means clustering. The chapter concludes with a discussion on the limitations of k-means clustering and discusses considerations while using this algorithm.

Exercise 1: Basics of k-means clustering Exercise 2: K-means clustering: first exercise Exercise 3: Runtime of k-means clustering Exercise 4: How many clusters?Exercise 5: Elbow method on distinct clusters Exercise 6: Elbow method on uniform data Exercise 7: Limitations of k-means clustering Exercise 8: Impact of seeds on distinct clusters Exercise 9: Uniform clustering patterns Exercise 10: FIFA 18: defenders revisited

Now that you are familiar with two of the most popular clustering techniques, this chapter helps you apply this knowledge to real-world problems. The chapter first discusses the process of finding dominant colors in an image, before moving on to the problem discussed in the introduction - clustering of news articles. The chapter concludes with a discussion on clustering with multiple variables, which makes it difficult to visualize all the data.

Exercise 1: Dominant colors in images Exercise 2: Extract RGB values from image Exercise 3: How many dominant colors?Exercise 4: Display dominant colors Exercise 5: Document clustering Exercise 6: TF-IDF of movie plots Exercise 7: Top terms in movie clusters Exercise 8: Clustering with multiple features Exercise 9: Clustering with many features Exercise 10: Basic checks on clusters Exercise 11: FIFA 18: what makes a complete player?Exercise 12: Farewell!