1. Introduction to Clustering

Activity 1.01: Implementing k-means Clustering

Solution:

1. Import the required libraries:

   from sklearn.datasets import make_blobs
   from sklearn.cluster import KMeans
   from sklearn.metrics import accuracy_score, silhouette_score
   import matplotlib.pyplot as plt
   import pandas as pd
   import numpy as np
   from scipy.spatial.distance import cdist
   import math
   np.random.seed(0)
   %matplotlib inline

2. Load the seeds data file using pandas:

   seeds = pd.read_csv('Seed_Data.csv')

3. Return the first five rows of the dataset, as follows:

   seeds.head()

   The output is as follows:

   Figure 1.25: Displaying the first five rows of the dataset

4. Separate the X features as follows:

   X = seeds[['A','P','C','LK','WK','A_Coef','LKG']]
   y = seeds['target']

5. Check the features as follows:

   X.head()

   The output is as follows:

   Figure 1.26: Printing the features

6. Define the k_means function as follows...