FREE MS-DS Master of Data science Data Wrangling and Preprocessing Questions and Answers

Question 1

A data scientist is preparing a dataset for a machine learning model that is sensitive to the scale of its input features, such as K-Nearest Neighbors.
The dataset contains two numerical features: 'Age' (ranging from 20 to 70) and 'Income' (ranging from 30,000 to 150,000).
Which of the following preprocessing techniques is most appropriate to apply to these features?

Accepted Answer

Normalization (Min-Max Scaling)

Answer

One-Hot Encoding

Answer

Standardization (Z-score normalization)

Answer

Log Transformation

Question 2

You are working with a customer dataset that includes a 'Subscription Level' feature with the categories: 'Bronze', 'Silver', 'Gold', and 'Platinum'. There is a clear hierarchical order to these categories. Which encoding method would be most suitable and efficient for this feature before using it in a machine learning model?

Accepted Answer

Label Encoding

Answer

One-Hot Encoding

Answer

Binary Encoding

Answer

Hashing Encoder

Question 3

A dataset containing sensor readings has missing values in a 'Temperature' column. The data is missing completely at random (MCAR), and the overall temperature distribution is not heavily skewed. To handle the missing data, a data scientist decides to use an imputation method. Which of the following is a simple, yet reasonable, imputation strategy in this scenario?

Accepted Answer

Replace missing values with the mean of the 'Temperature' column

Answer

Replace missing values with a constant like 0

Answer

Delete all rows with missing temperature values

Answer

Use K-Nearest Neighbors (KNN) Imputation

Question 4

Which of the following data wrangling tasks is primarily aimed at reducing the dimensionality of the dataset while retaining as much of the original variance as possible?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Outlier Removal using IQR

Answer

Feature Scaling using Standardization

Answer

Data Aggregation

Question 5

A data scientist is analyzing a dataset of house prices and notices that the 'LotArea' feature is highly right-skewed. To prepare this feature for a linear regression model, which assumes a more normal distribution of variables, what is the most appropriate transformation to apply?

Accepted Answer

Log Transformation

Answer

One-Hot Encoding

Answer

Standardization

Answer

Binning

Question 6

When cleaning a dataset, you discover that the 'Country' column, a categorical feature, has 50 unique values. You need to prepare this feature for a machine learning algorithm. If you use one-hot encoding, what potential problem are you most likely to introduce?

Accepted Answer

The Curse of Dimensionality

Answer

Loss of ordinal information

Answer

Introduction of multicollinearity if not handled correctly

Answer

Underfitting of the model