Küçük sayıların normalizasyonu

Önceki örneklerde, tam sayıların normalizasyonunu gördün. Bu egzersizde, kesirli sayılara nasıl yaklaşıldığını inceleyeceksin — Bangalla ülkesindeki faiz oranlarının yıllar içindeki değişimi. Kullanman için matplotlib.pyplot plt takma adıyla içe aktarılmıştır.

Bu egzersiz

Python ile Kümeleme Analizi

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Faiz oranı değişimlerini içeren rate_cuts listesini ölçeklendir.
Orijinal veriyi ölçeklenmiş veriye karşı çiz.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# 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()

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Kümeleme Analizi

kursunun bir parçasıdır

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Kursa Ücretsiz Başlayın

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: temel bilgiler Exercise 2: Gerçek dünyada unsupervised learning Exercise 3: Pokémon gözlemleri Exercise 4: Kümeleme analizinin temelleri Exercise 5: Pokémon görümleri: hiyerarşik kümeleme Exercise 6: Pokémon gözlemleri: k-means kümeleme Exercise 7: Kümeleme analizi için veri hazırlığı Exercise 8: Temel liste verisini normalize et Exercise 9: Normalize edilmiş veriyi görselleştir Exercise 10: Küçük sayıların normalizasyonu

Geçerli Egzersiz

Exercise 11: FIFA 18: Verileri normalize et

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!