Practical Data Science using Python - Logistic Regression - Telecom Churn Case Study

The video tutorial covers a logistic regression project aimed at solving a customer churn problem for a telecom company. It discusses the business problem, data preparation, and cleaning processes, including handling null values and data types. The tutorial also explores univariate analysis to gain insights into customer behavior and churn patterns.

What is the primary business problem the telecom company is facing?

• Losing customers to competitors

• Increasing operational costs

• Improving customer service

• Expanding into new markets

Which machine learning algorithm is chosen to solve the churn prediction problem?

• Decision Tree

• Logistic Regression

• Support Vector Machine

• Random Forest

How many separate files are used as input data for the analysis?

• Three

• Four

• One

• Two

What is the common identifier used to merge the datasets?

• Phone Number

• Email Address

• Customer ID

• Customer Name

What issue was found with the 'total charges' feature during preprocessing?

• It contained negative values

• It was missing in the dataset

• It had spaces instead of numeric values

• It was duplicated

What type of analysis is performed to understand the distribution of total charges?

• Bivariate Analysis

• Time Series Analysis

• Univariate Analysis

• Multivariate Analysis

What is the median tenure for customers who churn?

• 30 months

• 20 months

• 40 months

• 10 months

Which type of contract has the highest churn rate?

• Prepaid

• Two-year

• One-year

• Month-to-month

What is the relationship between monthly charges and churn?

• Lower monthly charges lead to higher churn

• Monthly charges have no effect on churn

• Higher monthly charges lead to higher churn

• Higher monthly charges lead to lower churn

Why might it be beneficial to drop the 'total charges' feature?

• It is a duplicate of another feature

• It is highly correlated with monthly charges and tenure

• It contains too many missing values

• It is not correlated with other features