Data Science Data Cleaning and Preparation Questions and Answers

Question 1

A data scientist is preparing a dataset for a K-Nearest Neighbors (KNN) model.
The dataset contains features with vastly different scales: 'Age' (ranging from 20-70) and 'Income' (ranging from 30,000-250,000).
Which data preparation technique is most crucial to apply in this scenario to ensure model performance is not biased?

Accepted Answer

Feature Scaling

Answer

One-Hot Encoding

Answer

Logarithmic Transformation

Answer

Principal Component Analysis

Question 2

A dataset contains a categorical feature 'Product_Category' with three unique values: 'Electronics', 'Apparel', and 'Groceries'. The machine learning model to be used is a linear regression model. Which encoding technique is most appropriate for this feature, and why?

Accepted Answer

One-Hot Encoding, because it prevents the model from assuming a false ordinal relationship between the categories.

Answer

Label Encoding, because it is computationally efficient and creates a single feature column.

Answer

Binary Encoding, because it is a memory-efficient compromise between Label and One-Hot encoding.

Answer

Hashing Encoding, because it handles a large number of categories without increasing dimensionality significantly.

Question 3

Which of the following methods for handling missing numerical data is generally most robust to the presence of outliers in the feature's distribution?

Accepted Answer

Median Imputation

Answer

Mean Imputation

Answer

Mode Imputation

Answer

Dropping the rows with missing values

Question 4

A data analyst is cleaning a dataset and identifies several data points that are three standard deviations away from the mean for a particular feature that is approximately normally distributed. What is this method of identifying potential issues called?

Accepted Answer

Z-Score Method

Answer

Interquartile Range (IQR) Method

Answer

DBSCAN Clustering

Answer

Isolation Forest

Question 5

A data scientist is working with a dataset where a numerical feature has a strong positive skew. To prepare this data for a model that performs better with normally distributed features, which transformation is most appropriate?

Accepted Answer

Logarithmic Transformation

Answer

Standardization (Z-score scaling)

Answer

Min-Max Normalization

Answer

One-Hot Encoding

Question 6

When cleaning a customer database, a data analyst discovers that the 'State' column contains inconsistencies such as 'CA', 'Calif.', and 'California'. What is the most appropriate data cleaning step to address this issue?

Accepted Answer

Standardize the categorical values to a single format.

Answer

Impute missing values using the mode.

Answer

Remove the 'State' column from the dataset.

Answer

Apply feature scaling to the 'State' column.