MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working on a project to predict customer churn for a telecom company. The dataset includes 50,000 records with 20 features, including customer demographics, account information, and service usage. The data scientist uses Amazon SageMaker Studio and loads the data into a pandas DataFrame. During EDA, the data scientist notices that the target variable 'churn' has only 10% positive cases. Additionally, several features have missing values: 'income' has 5% missing, 'age' has 2% missing, and 'total_charges' has 1% missing. The data scientist also observes that 'income' is highly skewed with a long right tail, and 'age' is moderately skewed. The data scientist wants to handle missing values and prepare the data for modeling. 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
✓
Impute 'income' with median, 'age' with median, 'total_charges' with median, and use SMOTE to handle class imbalance after splitting the data.
Median imputation is robust to skewness (particularly for income and age), and SMOTE is applied after splitting to avoid data leakage and handle class imbalance. Option B is wrong because removing rows with missing values would discard roughly 8% of the data (5%+2%+1% with potential overlap), which is a significant loss of information; additionally, random oversampling may lead to overfitting. Option C is wrong because mode imputation is appropriate for categorical data, not for continuous features like income, age, and total_charges. Option D is wrong because mean imputation is sensitive to outliers and skewness, especially for income with a long right tail; also, stratified sampling only ensures proportional representation in train/test splits, it does not generate synthetic samples to address imbalance.
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 'income' with median, 'age' with median, 'total_charges' with median, and use SMOTE to handle class imbalance after splitting the data.
Why this is correct
Median is robust to skewness. SMOTE is appropriate for imbalance.
- ✗
Remove all rows with any missing values, and use random oversampling to handle class imbalance.
Why it's wrong here
Removing 8% of data may lose valuable information.
- ✗
Impute 'income' with mode, 'age' with mode, 'total_charges' with mode, and use SMOTE after splitting.
Why it's wrong here
Mode is for categorical variables, not continuous.
- ✗
Impute all missing values with the mean of each column, and use stratified sampling to handle class imbalance.
Why it's wrong here
Mean imputation is affected by skewness.
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