seaborn ile kümeleri görselleştir

Şimdi seaborn modülünü kullanarak Comic Con’daki yaya trafiği (footfall) veri kümesini görselleştirelim. seaborn ile kümeleri görselleştirmek, küme etiketleri için yerleşik hue işlevi sayesinde daha kolaydır.

Veriler bir pandas DataFrame’i olan comic_con içinde. x_scaled ve y_scaled, belirli bir anda kişilerin standartlaştırılmış X ve Y koordinatlarının sütun adlarıdır. cluster_labels küme etiketlerini içerir. Bir bağlama (linkage) nesnesi distance_matrix değişkeninde saklanmıştır.

Bu egzersiz

Python ile Kümeleme Analizi

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Egzersiz talimatları

seaborn modülünü sns olarak içe aktar.
seaborn'un .scatterplot() metodunu kullanarak, hue argümanında küme etiketlerini vererek bir saçılım grafiği çiz.

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Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import the seaborn module
____

# Plot a scatter plot using seaborn
sns.scatterplot(x=____, 
                y=____, 
                ____=____, 
                data = comic_con)
plt.show()

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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: basics Exercise 2: Unsupervised learning in real world Exercise 3: Pokémon sightings Exercise 4: Basics of cluster analysis Exercise 5: Pokémon sightings: hierarchical clustering Exercise 6: Pokémon sightings: k-means clustering Exercise 7: Data preparation for cluster analysis Exercise 8: Normalize basic list data Exercise 9: Visualize normalized data Exercise 10: Normalization of small numbers Exercise 11: FIFA 18: Normalize 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: Hiyerarşik kümelemenin temelleri Exercise 2: Hiyerarşik kümeleme: ward yöntemi Exercise 3: Hiyerarşik kümeleme: single yöntemi Exercise 4: Hiyerarşik kümeleme: complete yöntemi Exercise 5: Kümeleri görselleştir Exercise 6: matplotlib ile kümeleri görselleştir Exercise 7: seaborn ile kümeleri görselleştir

Geçerli Egzersiz

Exercise 8: Kaç küme var?Exercise 9: Bir dendrogram oluştur Exercise 10: Comic Con verilerinde kaç küme var?Exercise 11: Hiyerarşik kümelemenin sınırlamaları Exercise 12: Hiyerarşik kümelemeyi çalıştırmanın süresini ölçme Exercise 13: FIFA 18: defans oyuncularını keşfetme

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!