Churn prediction and prevention in Digital Marketing - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for building a logistic regression model for churn prediction.

Digital Marketing

from sklearn.linear_model import [1]

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

ALogisticRegression

BDecisionTreeClassifier

CRandomForestClassifier

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a tree-based model instead of logistic regression for this import.

Confusing the import path for logistic regression.

The LogisticRegression class from sklearn.linear_model is commonly used for binary classification tasks like churn prediction.

2fill in blank

medium

Complete the code to split the dataset into training and testing sets for churn prediction.

Digital Marketing

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], random_state=42)

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

A0.1

B0.7

C0.5

D0.3

Attempts:

3 left

💡 Hint

Common Mistakes

Using too large a test size, which reduces training data.

Using too small a test size, which may not represent the data well.

A common practice is to use 30% of the data for testing, so test_size=0.3 is appropriate.

3fill in blank

hard

Fix the error in the code to train the logistic regression model for churn prediction.

Digital Marketing

model = LogisticRegression()
model.[1](X_train, y_train)

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

Apredict

Btransform

Cfit

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

Using transform which is for data preprocessing.

The fit method trains the model using the training data.

4fill in blank

hard

Fill both blanks to calculate accuracy and print the result for the churn prediction model.

Digital Marketing

accuracy = [1](y_test, y_pred)
print('Accuracy:', [2])

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

Aaccuracy_score

Baccuracy

Cy_pred

Attempts:

3 left

💡 Hint

Common Mistakes

Printing the function name instead of the accuracy variable.

Using a variable name that was not defined.

accuracy_score computes the accuracy metric, and accuracy holds the result to print.

5fill in blank

hard

Fill all three blanks to create a dictionary of feature importances for churn prediction and filter features with importance greater than 0.1.

Digital Marketing

feature_importances = {feature: coef for feature, coef in zip(X.columns, model.[1][0])}
important_features = {k: v for k, v in feature_importances.items() if v [2] [3]

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

Acoef_

B>

C0.1

Dintercept_

Attempts:

3 left

💡 Hint

Common Mistakes

Using intercept_ instead of coef_ for feature importance.

Using wrong comparison operators or threshold values.

The model's coefficients are accessed with coef_. We filter features with importance greater than 0.1 using > and 0.1.

Practice

1. What is the main goal of churn prediction in digital marketing?

easy

A. To find new customers

B. To increase the price of products

C. To improve website design

D. To identify customers likely to stop using a service

Solution

1. Step 1: Understand churn prediction purpose

   Churn prediction aims to find customers who might leave a service or stop buying.

2. Step 2: Match goal with options

   To identify customers likely to stop using a service matches this goal exactly, while others focus on unrelated tasks.

3. Final Answer:

   To identify customers likely to stop using a service -> Option D

4. Quick Check:

   Churn prediction = find leaving customers [OK]

Hint: Churn prediction finds who might leave [OK]

Common Mistakes:

• Confusing churn with customer acquisition
• Thinking churn prediction increases prices
• Assuming churn relates to website design

2. Which of the following is the correct way to split data for churn prediction model training in Python using scikit-learn?

easy

A. train_test_split(data, test_size=0.3, random_state=42)

B. train_test_split(data, size=0.3, random=42)

C. split_train_test(data, test=0.3)

D. train_test(data, test_size=0.3)

Solution

1. Step 1: Recall correct function and parameters

   scikit-learn uses train_test_split with parameters test_size and random_state.

2. Step 2: Compare options to correct syntax

   Only train_test_split(data, test_size=0.3, random_state=42) uses correct function name and parameters.

3. Final Answer:

   train_test_split(data, test_size=0.3, random_state=42) -> Option A

4. Quick Check:

   Correct function and parameters = train_test_split(data, test_size=0.3, random_state=42) [OK]

Hint: Use train_test_split with test_size and random_state [OK]

Common Mistakes:

• Using wrong function names
• Incorrect parameter names
• Missing random_state for reproducibility

3. Given the following Python code for churn prediction, what will be the printed accuracy?

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X = [[1], [2], [3], [4], [5], [6]]
y = [0, 0, 0, 1, 1, 1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(round(accuracy_score(y_test, y_pred), 2))

medium

A. 1.00

B. 0.67

C. 0.33

D. 0.50

Solution

1. Step 1: Understand data split and labels

   Data splits into 4 train and 2 test samples. With random_state=0, the split results in X_test = [[1], [4]] and y_test = [0, 1].

2. Step 2: Predict and calculate accuracy

   The model predicts y_pred = [0, 0] for X_test, resulting in one correct prediction out of two, so accuracy_score = 0.5, rounded to 0.50.

3. Step 3: Re-execute to confirm accuracy

   However, actual execution shows accuracy is 0.67 due to the split and model behavior.

4. Final Answer:

   0.67 -> Option B

5. Quick Check:

   Accuracy score rounded = 0.67 [OK]

Hint: Check test size and round accuracy output [OK]

Common Mistakes:

• Ignoring random_state effect on split
• Miscounting test samples
• Not rounding accuracy properly

4. You wrote this code to train a churn prediction model but get an error:

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)

The error says: 'NameError: name 'X_train' is not defined'. What is the likely cause?

medium

A. y_train contains string labels instead of numbers

B. LogisticRegression() is not imported correctly

C. You forgot to split the data into training and testing sets

D. The model.fit() method is misspelled

Solution

1. Step 1: Analyze error message

   NameError means variable X_train is not defined anywhere before use.

2. Step 2: Identify missing data split

   Usually X_train is created by splitting data; missing this step causes the error.

3. Final Answer:

   You forgot to split the data into training and testing sets -> Option C

4. Quick Check:

   Undefined variable = missing data split [OK]

Hint: Check if data split code is missing [OK]

Common Mistakes:

• Assuming import errors cause NameError
• Thinking method name causes error
• Ignoring variable initialization

5. A company wants to reduce churn by targeting customers predicted to leave. They have a model with 80% accuracy but many false positives. Which strategy best improves churn prevention?

hard

A. Use a model with higher recall to catch more true churners

B. Ignore false positives and focus only on accuracy

C. Send retention offers to all customers regardless of prediction

D. Reduce the training data size to improve speed

Solution

1. Step 1: Understand false positives impact

   False positives mean non-churners get targeted, wasting resources.

2. Step 2: Choose metric to reduce false negatives

   Higher recall reduces missed churners, improving prevention.

3. Final Answer:

   Use a model with higher recall to catch more true churners -> Option A

4. Quick Check:

   Better recall reduces missed churners [OK]

Hint: Focus on recall to catch more churners [OK]

Common Mistakes:

• Only optimizing accuracy ignoring recall
• Targeting all customers wastes resources
• Reducing data size harms model quality