Activities in Explore and Engineer Features
5 activities · 1 phase
| # | Name | Abbrev. | Guidance | Dependencies | Actions |
|---|---|---|---|---|---|
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Explore Data and Find Signal
5
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| 1 | Compute descriptive statistics per candidate variable | EFS-1 | For every candidate variable, compute sum/mean/median/std/count/min/max (with skipna), sort/rank it, and look at cumulative/period-over-period measures … | ||
| 2 | Choose candidate target variable(s) Y | EFS-2 | From the Framing Brief's questions, pick the concrete column(s) that would answer them if you … | ||
| 3 | Engineer and derive features | EFS-3 | Build the feature table: arithmetic transforms across columns (index-aligned), groupby() aggregation to compare Y across … | ||
| 4 | Cross-tabulate categorical candidates against Y | EFS-4 | Run pd.crosstab(feature, Y, margins=True) for each categorical feature candidate as a cheap first-pass independence check … | ||
| 5 | Compute correlation/covariance and significance between features and Y | EFS-5 | Compute pairwise correlation/covariance (frame.corr(), .cov(), .corrwith(Y)) across the feature table, then get significance via scipy.stats.pearsonr() … | ||