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Exercise

FIFA 18: exploring defenders

In the FIFA 18 dataset, various attributes of players are present. Two such attributes are:

  • sliding tackle: a number between 0-99 which signifies how accurate a player is able to perform sliding tackles
  • aggression: a number between 0-99 which signifies the commitment and will of a player

These are typically high in defense-minded players. In this exercise, you will perform clustering based on these attributes in the data.

This data consists of 5000 rows, and is considerably larger than earlier datasets. Running hierarchical clustering on this data can take up to 10 seconds.

The following modules are pre-loaded: dendrogram, linkage, fcluster from scipy.cluster.hierarchy, matplotlib.pyplot as plt, seaborn as sns. The data is stored in a Pandas dataframe, fifa.

Instructions 1/4
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  • Fit the scaled data in columns scaled_sliding_tackle and scaled_aggression into a hierarchical clustering algorithm. Additionally, you may want to check how long it takes to run the data in the console using the timeit module.
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