Genormaliseerde data visualiseren

Na het normaliseren van je data kun je de geschaalde data vergelijken met de oorspronkelijke data om het verschil te zien. De variabelen uit de vorige oefening, goals_for en scaled_data, zijn al voor je beschikbaar.

Deze oefening maakt deel uit van de cursus

Clusteranalyse in Python

Cursus bekijken

Oefeninstructies

Gebruik de bibliotheek matplotlib om de oorspronkelijke en de geschaalde data te plotten.
Toon de legenda in de plot.
Laat de plot zien.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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

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

# Show the legend in the plot
plt.____()

# Display the plot
plt.____()

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Clusteranalyse in Python

SkillTag.level.intermediateSkillTag.label

4.8+

841 reviews

Begin de cursus 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: de basis Exercise 2: Unsupervised learning in de echte wereld Exercise 3: Pokémon-waarnemingen Exercise 4: Basis van clusteranalyse Exercise 5: Pokémon-waarnemingen: hiërarchisch clusteren Exercise 6: Pokémon-waarnemingen: k-means clustering Exercise 7: Gegevens voorbereiden voor clusteranalyse Exercise 8: Normaliseer eenvoudige lijstgegevens Exercise 9: Genormaliseerde data visualiseren

Huidige oefening

Exercise 10: Normalisatie van kleine getallen Exercise 11: FIFA 18: Gegevens normaliseren

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!