Chapter 4: Dimensionality Reduction and Unsupervised Learning

Activity 12: Ensemble k-means Clustering and Calculating Predictions

Solution:

After the glass dataset has been imported, shuffled, and standardized (see Exercise 58):

1. Instantiate an empty data frame to append each model and save it as the new data frame object labels_df with the following code:

   import pandas as pd
   labels_df = pd.DataFrame()

2. Import the KMeans function outside of the loop using the following:

   from sklearn.cluster import KMeans

3. Complete 100 iterations as follows:

   for i in range(0, 100):

4. Save a KMeans model object with two clusters (arbitrarily decided upon, a priori) using:

   model = KMeans(n_clusters=2)

5. Fit the model to scaled_features using the following:

   model.fit(scaled_features)

6. Generate the labels array and save it as the labels object, as follows:

   labels = model.labels_

7. Store labels as a column in labels_df named after the iteration using the code:

   labels_df['Model_...