K-means clustering is one of the important and simple unsupervised machine learning algorithms.

In this the data item is categorize into k groups of similarity.

The algorithm works as:

• First, we initialize k points (called means, randomly).

• After this categorize each item to its closest mean and we update the mean’s coordinates, which are the averages of the items categorized in that mean so far.

• Then repeat the process to find our clusters.

# importing required libraries
import pandas as pd
from sklearn.cluster import KMeans
 
# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')
 
# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)
. 
 
model = KMeans() 
 
# fit the model with the training data
model.fit(train_data)
 
# Number of Clusters
print('\nDefault number of Clusters : ',model.n_clusters)
 
# predict the clusters on the train dataset
predict_train = model.predict(train_data)
print('\nCLusters on train data',predict_train) 
 
# predict the target on the test dataset
predict_test = model.predict(test_data)
print('Clusters on test data',predict_test) 
 
# Now, we will train a model with n_cluster = 3
model_n3 = KMeans(n_clusters=3)
 
# fit the model with the training data
model_n3.fit(train_data)
 
# Number of Clusters
print('\nNumber of Clusters : ',model_n3.n_clusters)
 
# predict the clusters on the train dataset
predict_train_3 = model_n3.predict(train_data)
print('\nCLusters on train data',predict_train_3) 
 
# predict the target on the test dataset
predict_test_3 = model_n3.predict(test_data)
print('Clusters on test data',predict_test_3)