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

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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.