Understanding Unsupervised Learning

                                
# Function to calculate distance between two points
def calculate_distance(point1, point2):
    distance_x = (point1[0] - point2[0]) ** 2
    distance_y = (point1[1] - point2[1]) ** 2
    total_distance = (distance_x + distance_y) ** 0.5
    return total_distance

                              

                                
from sklearn.cluster import KMeans

# Create a KMeans model with 3 clusters
model = KMeans(n_clusters=3)

# Fit the model to the data
model.fit(data_samples)

# Predict the cluster for each data point
cluster_labels = model.predict(data_samples)

                              

                                
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

# Sample data points
data = [[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]
inertias = []

# Test different numbers of clusters
for k in range(1, 6):
    model = KMeans(n_clusters=k)
    model.fit(data)
    inertias.append(model.inertia_)

# Plot the inertia against the number of clusters
plt.plot(range(1, 6), inertias, marker='o')
plt.xlabel('Number of Clusters')
plt.ylabel('Inertia')
plt.title('Elbow Method for Optimal K')
plt.show()