Chapter 3: Data Preprocessing and Feature Engineering

1. What is the purpose of data cleaning in data preprocessing? - a) To improve model performance by transforming features

• b) To identify and handle missing data, remove duplicates, and correct errors
• c) To scale data to a consistent range
• d) To reduce the dimensionality of the dataset

1. Which technique is typically used for handling missing data? - a) One-hot encoding

• b) Data augmentation
• c) Imputation
• d) PCA

1. Feature engineering involves which of the following? - a) Creating new features from existing ones

• b) Reducing noise from the data
• c) Increasing the number of samples in the dataset
• d) Both a and b

1. Why is it important to scale numerical features? - a) To remove outliers from the dataset

• b) To ensure features with different ranges contribute equally to model performance
• c) To increase the size of the dataset
• d) To remove noise from the dataset

1. What is the Train-Test Split used for? - a) Creating synthetic data samples

• b) Separating data into training and testing sets for model validation
• c) Increasing the number of features in the dataset
• d) Standardizing features to the same scale