Customer segmentation pattern in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for customer segmentation.

Data Analysis Python

import [1] as pd

Drag options to blanks, or click blank then click option'

Apandas

Bnumpy

Cmatplotlib

Dsklearn

Attempts:

3 left

💡 Hint

Common Mistakes

Using numpy instead of pandas for data tables.

Importing sklearn here instead of pandas.

We use pandas to handle data in tables, which is essential for customer segmentation.

2fill in blank

medium

Complete the code to load customer data from a CSV file.

Data Analysis Python

data = pd.[1]('customers.csv')

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Aread_excel

Bread_csv

Cread_json

Dread_table

Attempts:

3 left

💡 Hint

Common Mistakes

Using read_excel for CSV files.

Using read_json when the file is CSV.

To load data from a CSV file, we use pd.read_csv.

3fill in blank

hard

Fix the error in the code to select only numeric columns for clustering.

Data Analysis Python

numeric_data = data.select_dtypes(include=[1])

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A'float64'

B'numeric'

C'object'

D'number'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'object' which selects text columns.

Using 'float64' which selects only one numeric type.

The correct keyword to select numeric columns is 'number'.

4fill in blank

hard

Fill both blanks to create a KMeans model and fit it to the numeric data.

Data Analysis Python

from sklearn.cluster import [1]
model = [2](n_clusters=3)
model.fit(numeric_data)

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AKMeans

BLinearRegression

DLogisticRegression

Attempts:

3 left

💡 Hint

Common Mistakes

Using regression models instead of clustering.

Mismatching import and model names.

We import and create a KMeans clustering model to segment customers.

5fill in blank

hard

Fill both blanks to add cluster labels to the data and show the first 5 rows.

Data Analysis Python

data['cluster'] = model.[1](numeric_data)
print(data.[2](5))

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Apredict

Bhead

Ctail

Dfit_predict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict without fitting first.

Using tail instead of head to show rows.

We use fit_predict to assign clusters and head to display the first 5 rows.

Practice

1. What is the main goal of customer segmentation in data science?

easy

A. To group customers based on similar characteristics

B. To delete customer data from the database

C. To increase the number of customers randomly

D. To create a single profile for all customers

Solution

1. Step 1: Understand the purpose of segmentation

   Customer segmentation means dividing customers into groups with similar traits.

2. Step 2: Identify the correct goal

   The goal is to group customers, not delete or randomly increase them.

3. Final Answer:

   To group customers based on similar characteristics -> Option A

4. Quick Check:

   Segmentation = Grouping customers [OK]

Hint: Segmentation means grouping similar customers [OK]

Common Mistakes:

• Thinking segmentation deletes data
• Confusing segmentation with random customer increase
• Assuming segmentation creates one big group

2. Which Python library is commonly used for KMeans clustering in customer segmentation?

easy

A. scikit-learn

B. matplotlib

C. pandas

D. numpy

Solution

1. Step 1: Recall common libraries for clustering

   KMeans clustering is part of machine learning, often done with scikit-learn.

2. Step 2: Match library to KMeans

   scikit-learn provides KMeans; matplotlib is for plotting, pandas for data frames, numpy for arrays.

3. Final Answer:

   scikit-learn -> Option A

4. Quick Check:

   KMeans = scikit-learn [OK]

Hint: KMeans is in scikit-learn library [OK]

Common Mistakes:

• Choosing matplotlib for clustering
• Confusing pandas with clustering tools
• Selecting numpy instead of scikit-learn

3. What will be the output of this code snippet?

import pandas as pd
from sklearn.cluster import KMeans

data = pd.DataFrame({'Age': [25, 45, 35, 50], 'Income': [50000, 80000, 60000, 90000]})
model = KMeans(n_clusters=2, random_state=0)
model.fit(data)
print(model.labels_)

medium

A. [1 0 1 0]

B. [1 1 0 0]

C. [0 0 1 1]

D. [0 1 0 1]

Solution

1. Step 1: Understand KMeans clustering with 2 clusters

   The model groups similar rows into 2 clusters based on Age and Income.

2. Step 2: Predict cluster labels for each row

   Rows 0 and 2 are younger with lower income, likely cluster 1; rows 1 and 3 older with higher income, cluster 0.

3. Final Answer:

   [1 1 0 0] -> Option B

4. Quick Check:

   Similar customers grouped as [1 1 0 0] [OK]

Hint: Younger & lower income cluster 1, older & higher income cluster 0 [OK]

Common Mistakes:

• Assuming clusters are assigned randomly
• Mixing cluster labels order
• Forgetting random_state fixes output

4. Identify the error in this customer segmentation code:

from sklearn.cluster import KMeans
import pandas as pd

data = pd.DataFrame({'Age': [22, 34, 29], 'Income': [40000, 60000, 50000]})
model = KMeans(n_clusters=3)
model.fit(data)
print(model.labels_)

medium

A. KMeans requires data to be normalized before fitting

B. DataFrame columns should be strings, not numbers

C. No error; code runs correctly and prints labels

D. Missing random_state parameter in KMeans

Solution

1. Step 1: Check code syntax and usage

   Code imports libraries, creates DataFrame, fits KMeans with 3 clusters, and prints labels.

2. Step 2: Verify if code runs without error

   random_state is optional; columns as strings are correct; normalization is recommended but not required.

3. Final Answer:

   No error; code runs correctly and prints labels -> Option C

4. Quick Check:

   Code runs fine without random_state [OK]

Hint: random_state optional; code runs if syntax correct [OK]

Common Mistakes:

• Thinking random_state is mandatory
• Believing normalization is required to run
• Confusing column names with data types

5. You have customer data with Age, Income, and Spending Score. You want to create 3 segments using KMeans. Which steps should you follow to prepare and segment the data correctly?

hard

A. Remove Age column, set n_clusters=1, fit KMeans, ignore labels

B. Convert numeric data to strings, set n_clusters=3, fit KMeans, assign labels

C. Use raw data without scaling, set n_clusters=5, fit KMeans, assign labels

D. Normalize numeric columns, choose n_clusters=3, fit KMeans, assign labels to customers

Solution

1. Step 1: Prepare data by normalizing numeric columns

   Scaling ensures Age, Income, and Spending Score have equal influence on clustering.

2. Step 2: Set number of clusters and fit KMeans

   Choose n_clusters=3 as desired segments, then fit model and assign labels.

3. Final Answer:

   Normalize numeric columns, choose n_clusters=3, fit KMeans, assign labels to customers -> Option D

4. Quick Check:

   Scaling + correct clusters = proper segmentation [OK]

Hint: Always scale numeric data before KMeans clustering [OK]

Common Mistakes:

• Skipping normalization causing biased clusters
• Choosing wrong number of clusters
• Using non-numeric data without conversion