FIFA 18: Normalizzare i dati

FIFA 18 è un videogioco di calcio uscito nel 2017 per PC e console. L’insieme di dati su cui stai per lavorare contiene informazioni sui 1000 migliori giocatori del gioco. Nel corso vedrai varie caratteristiche dei dati. In questo esercizio lavorerai con due colonne: eur_wage, lo stipendio di un giocatore in euro, e eur_value, il suo attuale valore di mercato nel trasferimento.

I dati per questo esercizio sono salvati in un DataFrame di pandas, fifa. whiten da scipy.cluster.vq e matplotlib.pyplot come plt sono già stati caricati.

Questo esercizio fa parte del corso

Analisi di cluster in Python

Visualizza il corso

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Scale wage and value
fifa['scaled_wage'] = ____(fifa[____])
fifa['scaled_value'] = ____(fifa[____])

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Analisi di cluster in Python

IntermediárioNível de habilidade

4.8+

843 reviews

Inizia il corso 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: Machine Learning non supervisionato: nozioni di base Exercise 2: Unsupervised learning nel mondo reale Exercise 3: Avvistamenti di Pokémon Exercise 4: Basi dell'analisi dei cluster Exercise 5: Avvistamenti di Pokémon: clustering gerarchico Exercise 6: Avvistamenti di Pokémon: clustering k-means Exercise 7: Preparazione dei dati per l’analisi di clustering Exercise 8: Normalizza semplici dati in lista Exercise 9: Visualizza i dati normalizzati Exercise 10: Normalizzazione di numeri piccoli Exercise 11: FIFA 18: Normalizzare i dati

Esercizio in corso

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!