Chapter 1: Introduction to Clustering

Activity 1: Implementing k-means Clustering

Solution:

1. Load the Iris data file using pandas, a package that makes data wrangling much easier through the use of DataFrames:

   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt
   from sklearn.metrics import silhouette_score
   from scipy.spatial.distance import cdist
   iris = pd.read_csv('iris_data.csv', header=None)
   iris.columns = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'species']

2. Separate out the X features and the provided y species labels, since we want to treat this as an unsupervised learning problem:

   X = iris[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
   y = iris['species']

3. Get an idea of what our features look like:

   X.head()

   The output is as follows:

   Figure 1.22: First five rows of the data

4. Bring back the...