MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is building a model to predict housing prices using a dataset with 100,000 records and 50 features. The features include 'sqft_living', 'sqft_lot', 'bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade', etc. The data scientist uses Amazon SageMaker Data Wrangler for EDA. Upon reviewing the data, the data scientist finds that 'sqft_living' has a correlation of 0.7 with 'sqft_above' (square footage above ground) and 0.6 with 'sqft_basement'. Also, 'grade' (overall grade of the house) is highly correlated with 'condition' (0.8). The target variable 'price' is right-skewed. The data scientist plans to use a linear regression model. Which set of actions should the data scientist take to improve model performance?
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 target variable 'price' to reduce skewness, and remove either 'sqft_above' or 'sqft_living' and either 'grade' or 'condition' to handle multicollinearity.
Log-transforming the right-skewed target variable 'price' helps meet the normality assumption of linear regression residuals. Additionally, removing either 'sqft_above' or 'sqft_living' (correlated 0.7) and either 'grade' or 'condition' (correlated 0.8) reduces multicollinearity, which can destabilize coefficient estimates. Option A is incorrect because standard scaling does not address skewness or multicollinearity. Option B is incorrect because removing all features with correlation >0.5 is too aggressive and may discard useful information. Option C is incorrect because PCA reduces dimensionality but the components may be less interpretable, and log transformation is still needed for the target.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply standard scaling to all numeric features and use the data as is, since linear regression is robust to multicollinearity.
Why it's wrong here
Linear regression is sensitive to multicollinearity.
- ✗
Remove all features that have correlation >0.5 with any other feature to eliminate multicollinearity, and apply standard scaling to all numeric features.
Why it's wrong here
Removing all correlated features may discard important information.
- ✗
Apply principal component analysis (PCA) to all features to reduce dimensionality, and then fit linear regression on the principal components.
Why it's wrong here
PCA may not be necessary and reduces interpretability.
- ✓
Apply log transformation to the target variable 'price' to reduce skewness, and remove either 'sqft_above' or 'sqft_living' and either 'grade' or 'condition' to handle multicollinearity.
Why this is correct
Log transform addresses skewness; removing one of each pair reduces multicollinearity.
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