Hierarchical clustering (linkage) in SciPy - Mini Project: Build & Apply

Hierarchical clustering (linkage)

📖 Scenario: You work in a small shop that sells fruits. You want to group similar fruits based on their sweetness and crunchiness scores to understand which fruits are alike.

🎯 Goal: Build a simple hierarchical clustering using linkage to group fruits by their sweetness and crunchiness.

📋 What You'll Learn

Create a dictionary with fruit names as keys and their sweetness and crunchiness scores as values.

Create a list of fruit names to keep track of the order.

Use scipy's linkage function to perform hierarchical clustering on the fruit scores.

Print the linkage matrix to see the clustering result.

💡 Why This Matters

🌍 Real World

Hierarchical clustering helps group similar items, like fruits, customers, or documents, based on their features.

💼 Career

Data scientists use hierarchical clustering to find natural groups in data, which helps in marketing, biology, and many other fields.

1

Create the fruit data dictionary

Create a dictionary called fruit_data with these exact entries: 'Apple': [7, 6], 'Banana': [10, 2], 'Cherry': [6, 7], 'Date': [9, 3], 'Elderberry': [5, 8].

SciPy

# Create the fruit_data dictionary with sweetness and crunchiness scores
# Your code here

Need a hint?

Use curly braces to create a dictionary. Each fruit name is a key, and the value is a list with two numbers.

2

Create a list of fruit names

Create a list called fruit_names containing the fruit names in this exact order: 'Apple', 'Banana', 'Cherry', 'Date', 'Elderberry'.

SciPy

fruit_data = {
    'Apple': [7, 6],
    'Banana': [10, 2],
    'Cherry': [6, 7],
    'Date': [9, 3],
    'Elderberry': [5, 8]
}
# Create the fruit_names list with the fruit names in order
# Your code here

Need a hint?

Use square brackets to create a list with the fruit names in the given order.

3

Perform hierarchical clustering using linkage

Import linkage from scipy.cluster.hierarchy. Create a list called data_points that contains the fruit scores in the order of fruit_names. Then use linkage(data_points, method='single') to perform hierarchical clustering and save the result in a variable called linkage_matrix.

SciPy

fruit_data = {
    'Apple': [7, 6],
    'Banana': [10, 2],
    'Cherry': [6, 7],
    'Date': [9, 3],
    'Elderberry': [5, 8]
}

fruit_names = ['Apple', 'Banana', 'Cherry', 'Date', 'Elderberry']

# Import linkage and create data_points list
# Use linkage with method='single' to create linkage_matrix
# Your code here

Need a hint?

Use a list comprehension to get the scores in the order of fruit_names. Then call linkage with method='single'.

4

Print the linkage matrix

Print the variable linkage_matrix to display the hierarchical clustering result.

SciPy

from scipy.cluster.hierarchy import linkage

fruit_data = {
    'Apple': [7, 6],
    'Banana': [10, 2],
    'Cherry': [6, 7],
    'Date': [9, 3],
    'Elderberry': [5, 8]
}

fruit_names = ['Apple', 'Banana', 'Cherry', 'Date', 'Elderberry']

data_points = [fruit_data[name] for name in fruit_names]

linkage_matrix = linkage(data_points, method='single')

# Print the linkage_matrix
# Your code here

Need a hint?

Use print(linkage_matrix) to show the clustering result.