MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a time-series dataset and observes a strong upward trend and seasonal patterns. The scientist needs to make the data stationary for modeling. Which transformation should be applied?
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 differencing to the series
Differencing is a technique that removes trends and seasonality by subtracting the previous observation from the current one, making the time series stationary. One-hot encoding (A) is used for categorical variables. PCA (B) reduces dimensionality but does not address stationarity. Min-max scaling (C) normalizes the range of data but does not remove trend or seasonality. Logarithmic transformation (E) stabilizes variance but does not eliminate trends. Therefore, differencing (D) is the correct choice.
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 one-hot encoding
Why it's wrong here
One-hot encoding is for categorical features, not time series.
- ✗
Apply PCA
Why it's wrong here
PCA is for dimensionality reduction, not stationarity.
- ✗
Apply min-max scaling
Why it's wrong here
Scaling does not address non-stationarity.
- ✓
Apply differencing to the series
Why this is correct
Differencing removes trends and seasonality, making the series stationary.
- ✗
Apply logarithmic transformation
Why it's wrong here
Log transformation stabilizes variance but does not remove trends.
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