MCQs

1. What is the main purpose of the preprocessing and feature engineering in this project?
a. To visualize the dataset
b. To convert raw customer data into a format suitable for modeling
c. To deploy the model
d. To remove duplicate rows
Answer: b. To convert raw customer data into a format suitable for modeling
Preprocessing ensures that numerical, categorical, and derived features are properly prepared for the logistic regression model.

2. Why is Logistic Regression used in this project?
a. It reduces memory usage
b. It predicts churn probability efficiently
c. It visualizes correlations
d. It handles missing values automatically
Answer: b. It predicts churn probability efficiently
Logistic Regression provides interpretable probability scores for binary classification tasks like churn prediction.

3. What does the churn probability output represent?
a. Random guess of churn
b. Likelihood that a customer will churn
c. Average monthly charges
d. Tenure group classification
Answer: b. Likelihood that a customer will churn
The model outputs a probability between 0 and 1 indicating how likely a customer is to leave the service.

4. Why are TenureGroup and AvgMonthlySpend features created?
a. To reduce dataset size
b. To improve prediction accuracy by adding derived insights
c. To deploy the model faster
d. To store raw CSV data
Answer: b. To improve prediction accuracy by adding derived insights
Feature engineering helps the model capture patterns in tenure, spending, and service usage.

5. What is the main advantage of using Gradio for this project?
a. It replaces machine learning logic
b. It requires frontend coding
c. It quickly converts Python functions into an interactive web app
d. It stores datasets
Answer: c. It quickly converts Python functions into an interactive web app
Gradio enables users to interact with the churn prediction model without writing any frontend code.