FIFA 18: mengeksplorasi pemain bertahan

Dalam himpunan data FIFA 18, tersedia berbagai atribut pemain. Dua di antaranya adalah:

sliding tackle: angka antara 0–99 yang menunjukkan seberapa akurat seorang pemain melakukan tekel sapuan
aggression: angka antara 0–99 yang menunjukkan tingkat komitmen dan kemauan seorang pemain

Keduanya biasanya tinggi pada pemain yang berorientasi bertahan. Pada latihan ini, Anda akan melakukan pengelompokan berdasarkan atribut-atribut tersebut dalam data.

Data ini terdiri atas 5000 baris, dan jauh lebih besar dibandingkan himpunan data sebelumnya. Menjalankan hierarchical clustering pada data ini dapat memakan waktu hingga 10 detik.

Modul berikut telah dimuat: dendrogram, linkage, fcluster dari scipy.cluster.hierarchy, matplotlib.pyplot sebagai plt, seaborn sebagai sns. Data disimpan dalam pandas DataFrame, fifa.

Latihan ini adalah bagian dari kursus

Analisis Klaster di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Fit the data into a hierarchical clustering algorithm
distance_matrix = ____(fifa[[____, ____]], 'ward')

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Analisis Klaster di Python

SkillTag.level.intermediateSkillTag.label

4.8+

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: 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: Dasar-dasar hierarchical clustering Exercise 2: Hierarchical clustering: metode ward Exercise 3: Hierarchical clustering: metode single Exercise 4: Hierarchical clustering: metode complete Exercise 5: Visualisasikan klaster Exercise 6: Visualisasikan klaster dengan matplotlib Exercise 7: Visualisasikan klaster dengan seaborn Exercise 8: Berapa banyak klaster?Exercise 9: Buat dendrogram Exercise 10: Berapa banyak klaster pada data comic con?Exercise 11: Keterbatasan hierarchical clustering Exercise 12: Mengukur waktu eksekusi hierarchical clustering Exercise 13: FIFA 18: mengeksplorasi pemain bertahan

Latihan Saat Ini

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!