MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset of online retail transactions. The dataset contains 500,000 rows and 10 columns: 'TransactionID', 'CustomerID', 'ProductID', 'Quantity', 'UnitPrice', 'TransactionDate', 'PaymentMethod', 'ShippingAddress', 'Country', and 'TotalAmount'. The data scientist loads the data into a SageMaker notebook and performs initial EDA. The data scientist finds that 'UnitPrice' has a range from $0.01 to $10,000, with a mean of $50 and a median of $20. 'Quantity' ranges from -10 to 100, with negative values indicating returns. 'TotalAmount' is calculated as Quantity * UnitPrice. The data scientist also notices that 2% of the 'CustomerID' values are missing, and 1% of 'ProductID' values are missing. There are no missing values in other columns. The data scientist wants to clean the data and prepare it for customer segmentation. Which course of action is most appropriate?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove rows with negative 'Quantity' to focus on purchases. Impute missing 'CustomerID' and 'ProductID' with a placeholder such as 'Unknown'.
The most appropriate approach. Negative quantities represent returns, which should be removed when analyzing purchase behavior for customer segmentation. Imputing missing 'CustomerID' and 'ProductID' with a placeholder like 'Unknown' retains data without guessing categorical values. Option A is incorrect because mean imputation is not valid for categorical 'CustomerID'. Option B is incorrect because removing all rows with missing values would discard valuable data. Option C is incorrect because negative quantities are meaningful returns, not errors, and replacing them distorts the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Impute missing 'CustomerID' with the mean of 'CustomerID' and missing 'ProductID' with the mode.
Why it's wrong here
CustomerID is categorical; mean is invalid.
- ✗
Remove all rows with any missing values.
Why it's wrong here
Removing 3% of data may lose valuable information.
- ✗
Keep negative 'Quantity' and treat them as errors; replace them with the median of positive quantities.
Why it's wrong here
Negative quantities represent returns and should be handled separately.
- ✓
Remove rows with negative 'Quantity' to focus on purchases. Impute missing 'CustomerID' and 'ProductID' with a placeholder such as 'Unknown'.
Why this is correct
Negative quantities are returns; imputing with 'Unknown' preserves rows.
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