Practical Data Science using Python - Naive Bayes - Employee Attrition Case Study

The video tutorial addresses the employee attrition problem using a dataset containing various employee features. The goal is to predict whether an employee will leave the company using a Gaussian Naive Bayes model. The tutorial covers data preprocessing, including handling missing values and transforming categorical variables into numerical equivalents using dummy variables. It explains the importance of avoiding multicollinearity by dropping one dummy variable and concludes with preparing the final dataset for modeling.

What is the primary focus of the employee attrition problem discussed in the video?

• Predicting employee promotion

• Predicting employee performance

• Predicting employee attrition

• Predicting employee satisfaction

Which feature is NOT considered significant in the employee attrition problem?

• Age

• Employee count

• Business travel

• Daily compensation

Which model is used to predict employee attrition in the video?

• Gaussian Naive Bayes

• Decision Tree

• K-Nearest Neighbors

• Linear Regression

What is the target variable in the employee attrition problem?

• Employee promotion

• Attrition (Yes/No)

• Employee performance

• Employee satisfaction

What is the first step in processing the data for the attrition model?

• Reading the CSV file

• Normalizing the data

• Visualizing the data

• Dropping irrelevant columns

Why is it important to transform categorical variables into numerical equivalents?

• To make them compatible with the model

• To increase data complexity

• To improve data visualization

• To reduce data size

What does the 'replace' function do in the preprocessing step?

• Removes duplicate entries

• Converts categorical data to numerical

• Replaces missing values with averages

• Converts numerical data to text

What technique is used to handle non-ordinal categorical variables?

• Normalization

• Standardization

• Creating dummy variables

• Feature scaling

Why is it necessary to drop one of the dummy variables?

• To reduce data size

• To avoid multicollinearity

• To improve model accuracy

• To simplify data visualization

What is the final step before modeling in the video?

• Splitting the data into training and test sets

• Concatenating dummy variables with numeric features

• Creating visualizations

• Normalizing the data