MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is building a fraud detection model using a dataset of 500,000 credit card transactions. The dataset contains 20 features, including transaction amount, merchant category, time since last transaction, and customer age. The target variable 'is_fraud' has 0.1% positive examples. Initial EDA reveals that the transaction amount distribution is highly skewed with a long tail. Also, there are missing values in the 'customer_age' field (5% missing). The data scientist needs to prepare the data for training a binary classifier. Which combination of preprocessing steps should the data scientist apply to address these issues and improve model performance? (Select TWO.)
⚠ Common exam trap
Many candidates confuse handling skewness with scaling—they may choose standard scaling (Option B) thinking it addresses skewness, but standard scaling only centers and scales the data, not corrects the shape of the distribution.
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
✓
Apply log transformation to the transaction amount to reduce skewness.
This is a multi-select question requiring two correct preprocessing steps. Option C is correct because applying a log transformation to the highly skewed transaction amount reduces skewness and compresses the dynamic range, which helps many machine learning algorithms (especially those sensitive to feature scales like logistic regression or SVM) converge faster and perform better. Option D is correct because imputing missing values in customer_age with the mean is a simple and effective method when the missing rate is only 5% and the data is roughly normally distributed, preserving sample size. Option A is wrong because SMOTE is typically applied after splitting the data to avoid data leakage, and it is not a preprocessing step for EDA; also, the class imbalance is severe but SMOTE may be considered later. Option B is wrong because standard scaling does not handle skewness; it should be applied after skewness correction. Option E is wrong because dropping the feature due to skewness would lose valuable information; transformation is preferable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SMOTE to generate synthetic samples of the minority class.
Why it's wrong here
SMOTE addresses class imbalance, not skewness or missing values. The question specifically asks for steps to handle skewness and missing values.
- ✗
Apply standard scaling to all numerical features.
Why it's wrong here
Standard scaling does not reduce skewness and is not necessary for all models (e.g., tree-based models).
- ✓
Apply log transformation to the transaction amount to reduce skewness.
Why this is correct
Log transformation is effective for reducing right skewness and can make the distribution more Gaussian-like, which benefits many models.
- ✓
Impute missing values in customer_age with the mean of the non-missing values.
Why this is correct
Mean imputation is a common method for handling missing numerical data when the missing rate is low (5%).
- ✗
Drop the transaction amount feature because of its skewness.
Why it's wrong here
Dropping a potentially important feature like transaction amount is not advisable; transformations should be attempted first.
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