Data Science Data Science Data Cleaning and Preparation Questions and Answers 3

Question 1

What is the main advantage of using K-Nearest Neighbors imputation over simple mean imputation for missing data?

Accepted Answer

It considers relationships between features to produce context-aware estimates

Answer

KNN imputation leverages feature correlations by finding similar records, producing more realistic imputed values than a single global mean.

Question 2

When merging two datasets with different date formats (MM/DD/YYYY and YYYY-MM-DD), what should be done first?

Accepted Answer

Parse both columns into a standardized datetime format before merging

Answer

Standardizing date formats before merging prevents mismatches, join failures, and incorrect date comparisons.

Question 3

Which technique helps detect duplicate records that have slight variations in text fields such as customer names?

Accepted Answer

Fuzzy string matching using algorithms like Levenshtein distance

Answer

Fuzzy matching algorithms measure string similarity and can identify near-duplicates like 'Jon Smith' and 'John Smith' that exact matching would miss.

Question 4

What problem does multicollinearity cause during data preparation for regression models?

Accepted Answer

Unstable coefficient estimates that change dramatically with small data variations

Answer

Multicollinearity inflates the variance of coefficient estimates, making them unreliable and sensitive to minor changes in the data.

Question 5

What is the purpose of applying a Box-Cox transformation during data preparation?

Accepted Answer

To make a skewed distribution more closely approximate a normal distribution

Answer

Box-Cox transformation applies a power transformation that reduces skewness and helps data better satisfy normality assumptions required by many statistical methods.

Data Science Practice Test

Data Science Practice Test

Data Science Data Science Data Cleaning and Preparation Questions and Answers 3