MLS-C01 Exploratory Data Analysis Practice Question
A data science team at a financial services company is building a fraud detection model using a dataset of credit card transactions. The dataset contains 10 million rows and 20 features, including transaction amount, merchant category, time since last transaction, and customer ID. The target variable 'is_fraud' is highly imbalanced: only 0.1% of transactions are fraudulent. The team is performing exploratory data analysis (EDA) on a sample of 100,000 rows. They compute the correlation matrix and find that 'transaction amount' has a correlation of 0.02 with 'is_fraud'. They also plot the distribution of 'transaction amount' and see that it is heavily right-skewed with a long tail. The team wants to understand the relationship between 'transaction amount' and fraud more deeply before feature engineering. They have access to AWS SageMaker and can run processing jobs. 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
✓
Create bins for 'transaction amount' (e.g., 0-10, 10-50, 50-100, 100+) and compute the fraud rate per bin to detect any non-linear patterns
Binning the transaction amount and computing fraud rates per bin can reveal non-linear relationships that correlation might miss. Option A is wrong because concluding non-predictiveness based solely on correlation ignores potential non-linear patterns. Option B is premature since feature importance from a random forest model is typically used after feature engineering, not during initial EDA. Option D is a data transformation that addresses skewness but does not directly help understand the relationship with the target; it would be more appropriate as a preprocessing step.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Conclude that 'transaction amount' is not predictive because the correlation is near zero
Why it's wrong here
Low correlation does not rule out non-linear predictive power.
- ✗
Train a random forest model on the sample and use feature importance to assess the predictive power of 'transaction amount'
Why it's wrong here
Training a random forest on the sample would yield feature importance scores, but these reflect predictive power within the model's own splits, not a direct, interpretable measure of the linear or monotonic relationship between 'transaction amount' and 'is_fraud'. The team's stated goal is to understand the relationship *before* feature engineering, not to evaluate a model's reliance on the feature. This approach is tempting because random forest feature importance is a standard technique for ranking feature relevance in supervised learning, and would be correct if the team wanted to select features for a downstream model rather than explore the raw bivariate association.
- ✓
Create bins for 'transaction amount' (e.g., 0-10, 10-50, 50-100, 100+) and compute the fraud rate per bin to detect any non-linear patterns
Why this is correct
Binning and examining fraud rates per bin can reveal non-linear relationships.
- ✗
Apply a log transformation to 'transaction amount' to reduce skewness and re-run the correlation analysis
Why it's wrong here
Log transformation changes the distribution but still only measures linear correlation.
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