Practical Data Science using Python - Logistic Regression - Data Analysis and Feature Engineering

The video tutorial explores the impact of different internet connectivity types on customer churn rates, highlighting that fiber optic users tend to churn more. It discusses data processing techniques, including converting binary categorical features to numeric values and handling nominal categorical features with dummy variables. The tutorial also covers data cleaning, splitting data into training and test sets, and applying logistic regression with scaling to ensure uniformity across features.

What percentage of people with single internet connectivity churn?

• 25%

• 28%

• 41%

• 7.4%

Which type of internet service has the highest churn rate?

• Fiber Optic

• DSL

• No Internet Service

• Multiple Lines

Why is it important for service providers to encourage internet service usage despite churn concerns?

• To decrease operational costs

• To increase revenue streams

• To modernize infrastructure

• To reduce churn rates

What is the purpose of converting 'yes' and 'no' values to 1s and 0s?

• To simplify data entry

• To improve data accuracy

• To prepare data for numerical analysis

• To reduce data size

What is one-hot encoding used for?

• Converting binary features

• Handling missing values

• Encoding nominal categorical features

• Scaling data

Why is the 'drop first' parameter used in one-hot encoding?

• To reduce data size

• To avoid multicollinearity

• To improve model accuracy

• To simplify data processing

What is the main reason for scaling data in machine learning?

• To increase data security

• To ensure uniform feature scales

• To improve data visualization

• To reduce computation time

Which scaling method ensures that all feature values are within one standard deviation?

• Logarithmic Scaling

• Normalization

• Standard Scaling

• Min-Max Scaling

What is the effect of different feature scales on gradient descent?

• It has no effect

• It complicates the optimization process

• It speeds up convergence

• It simplifies the model

What does the standard scaler do to the data?

• Normalizes data to a range of 0 to 1

• Increases data variance

• Centers data around zero with unit variance

• Reduces data dimensionality