5. Analyzing the Online Shopper's Purchasing Intention

Activity 5.01: Performing K-means Clustering for Administrative Duration versus Bounce Rate and Administrative Duration versus Exit Rate

1. Select the Administrative Duration and Bounce Rate columns. Assign the column to a variable called x:

   x = df.iloc[:, [1, 6]].values
   x.shape

2. Initialize the k-means algorithm:

   wcss = []
   for i in range(1, 11):
       km = KMeans(n_clusters = i, init = 'k-means++', \
                   max_iter = 300, n_init = 10, random_state = 0, \
                   algorithm = 'elkan', tol = 0.001)

3. For the different values of K, compute the Kmeans inertia and store it in a variable called wcss:

       km.fit(x)
       labels = km.labels_
       wcss.append...