Performance evaluation for unsupervised algorithms

In this section, you will learn how to evaluate the performance of an unsupervised machine learning algorithm, such as the k-means algorithm. The first step is to build a simple k-means model. We can do so by using the following code:

#Reading in the dataset

df = pd.read_csv('fraud_prediction.csv')

#Dropping the target feature & the index

df = df.drop(['Unnamed: 0', 'isFraud'], axis = 1)

#Initializing K-means with 2 clusters

k_means = KMeans(n_clusters = 2)

Now that we have a simple k-means model with two clusters, we can proceed to evaluate the model's performance. The different visual performance charts that can be deployed are as follows:

• Elbow plot
• Silhouette analysis plot

In this section, you will learn how to create and interpret each of the preceding plots.

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