Clustering with multiple features

Cluster Analysis in Python

Shaumik Daityari

Business Analyst

Basic checks

# Cluster centers
print(fifa.groupby('cluster_labels')[['scaled_heading_accuracy', 
    'scaled_volleys', 'scaled_finishing']].mean())

cluster_labels	scaled_heading_accuracy	scaled_volleys	scaled_finishing
0	3.21	2.83	2.76
1	0.71	0.64	0.58

# Cluster sizes
print(fifa.groupby('cluster_labels')['ID'].count())

cluster_labels	count
0	886
1	114

Visualizations

Visualize cluster centers
Visualize other variables for each cluster

# Plot cluster centers
fifa.groupby('cluster_labels') \
  [scaled_features].mean()
  .plot(kind='bar')
plt.show()

Top items in clusters

# Get the name column of top 5 players in each cluster
for cluster in fifa['cluster_labels'].unique():
    print(cluster, fifa[fifa['cluster_labels'] == cluster]['name'].values[:5])

Cluster Label	Top Players
0	['Cristiano Ronaldo' 'L. Messi' 'Neymar' 'L. Suárez' 'R. Lewandowski']
1	['M. Neuer' 'De Gea' 'G. Buffon' 'T. Courtois' 'H. Lloris']

Feature reduction

Factor analysis
Multidimensional scaling

Final exercises!

Cluster Analysis in Python