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]

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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)

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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)

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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])

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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]

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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.