Data Science with Python Feature Engineering Techniques Questions and Answers

Question 1

A data scientist is preparing a dataset for a K-Nearest Neighbors (KNN) model.
The dataset contains an 'age' feature (range 20-70) and an 'income' feature (range 30,000-250,000).
Since KNN is a distance-based algorithm, what is the most appropriate feature scaling technique to apply and why?

Accepted Answer

Standardization (Z-score scaling), because it is less sensitive to outliers than Min-Max scaling and handles features on vastly different scales effectively.

Answer

One-Hot Encoding, because it converts features into a binary format that is easier to process.

Answer

Min-Max Scaling, because it preserves the original distribution of the data without distortion.

Answer

Log Transformation, because it reduces the right-skewness often found in income data.

Question 2

You are working with a categorical feature 'product_category' which contains the values 'Electronics', 'Apparel', 'Home Goods', and 'Books'. There is no inherent order or ranking among these categories. Which encoding technique should be used to prepare this feature for a linear regression model to prevent the model from assuming a false ordinal relationship?

Accepted Answer

One-Hot Encoding, as it creates separate binary columns for each category, avoiding any implied ranking.

Answer

Label Encoding, as it is computationally efficient and assigns a unique integer to each category.

Answer

Binary Encoding, as it is more memory-efficient than One-Hot Encoding for high-cardinality features.

Answer

Frequency Encoding, as it replaces categories with their count in the dataset.

Question 3

A dataset contains a 'last_login_date' column with a `datetime64[ns]` dtype. Which of the following feature engineering approaches is most effective for extracting cyclical patterns that could be useful for a predictive model?

Accepted Answer

Creating new numerical features such as 'day_of_week', 'month_of_year', and a binary 'is_weekend' flag.

Answer

Converting the entire 'last_login_date' column into a single integer representing the Unix timestamp.

Answer

Applying a log transformation to the date column to normalize its distribution.

Answer

Dropping the column, as datetime objects cannot be used directly in most machine learning models.

Question 4

A feature representing 'customer_spending' in a dataset is heavily right-skewed, with most values being low but with a long tail of very high-spending customers. Many linear machine learning models perform better with normally distributed features. What is a common and effective transformation to apply to this feature to make its distribution more symmetric?

Accepted Answer

Applying a logarithmic transformation (e.g., `np.log1p`) to compress the higher values and expand the lower values.

Answer

Applying Standardization, which will center the data around a mean of 0.

Answer

Using binning to group the spending values into discrete categories like 'low', 'medium', and 'high'.

Answer

Using Min-Max scaling to scale all values to a fixed range between 0 and 1.

Question 5

What is a primary advantage of using binning (or discretization) to transform a continuous numerical feature, such as 'Age', into categorical bins (e.g., '18-25', '26-40', '41-60')?

Accepted Answer

It can help a linear model capture non-linear relationships between the feature and the target variable.

Answer

It guarantees that the resulting feature will have a normal distribution.

Answer

It significantly reduces the memory footprint of the dataset by storing strings instead of integers.

Answer

It is the only way to handle missing values within a continuous feature.

Question 6

In a dataset for predicting house prices, you have features for 'num_bedrooms' and 'num_bathrooms'. Your initial model is underperforming, and you suspect the combined effect of these two features is more important than their individual effects. Which feature engineering technique would best capture this combined effect?

Accepted Answer

Creating an interaction feature, for example, by multiplying 'num_bedrooms' by 'num_bathrooms'.

Answer

Applying standardization to both features to put them on the same scale.

Answer

Creating polynomial features for each variable independently (e.g., bedrooms squared).

Answer

Binning each feature into categories like 'low', 'medium', and 'high'.