General
PromptBeginner5 minmarkdown
| Categorical
low cardinality | Mode imputation |
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General
PromptBeginner5 minmarkdown
| Numerical
skewed dist | Median imputation |
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General
PromptBeginner5 minmarkdown
| Numerical
time-series | Forward fill / Interpolation |
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General
PromptBeginner5 minmarkdown
| Numerical
symmetric dist | Mean imputation |
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General
PromptBeginner5 minmarkdown
→ Check for disguised missing values (?
N/A
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General
PromptBeginner5 minmarkdown
- Any disguised nulls found (?
N/A
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General
PromptBeginner5 minmarkdown
assert remaining_test.sum() == 0
fTest still has missing:\n{remaining_test[remaining_test > 0]}
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General
PromptBeginner5 minmarkdown
After completing Phases 1–4
deliver this exact report:
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General
PromptBeginner5 minmarkdown
[Column] — Reason: [e.g.
72% missing
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General
PromptBeginner5 minmarkdown
# STEP 12 — MICE / IterativeImputer (most powerful
use when needed)
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General
PromptBeginner5 minmarkdown
assert remaining_train.sum() == 0
fTrain still has missing:\n{remaining_train[remaining_train > 0]}
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General
PromptBeginner5 minmarkdown
# imp_iter = IterativeImputer(max_iter=10
random_state=42)
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General
PromptBeginner5 minmarkdown
X
y
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General
PromptBeginner5 minmarkdown
X_test = X_test.drop(columns=drop_cols
errors='ignore')
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General
PromptBeginner5 minmarkdown
X_train = X_train.drop(columns=drop_cols
errors='ignore')
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General
PromptBeginner5 minmarkdown
df.replace(DISGUISED_NULLS
np.nan
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General
PromptBeginner5 minmarkdown
df.dropna(subset=[TARGET_COL]
axis=0
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General
PromptBeginner5 minmarkdown
X_train
X_test
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General
PromptBeginner5 minmarkdown
DISGUISED_NULLS = [?
N/A
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General
PromptBeginner5 minmarkdown
│ SVM
KNN Classifier: │
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General
PromptBeginner5 minmarkdown
│ Tree-based (XGBoost
LightGBM
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General
PromptBeginner5 minmarkdown
│ → Creates: col_was_missing = 1 if NaN
else 0 │
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General
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│ Linear Models (LogReg
LinearReg
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General
PromptBeginner5 minmarkdown
→ df.dropna(subset=[TARGET_COL]
inplace=True)
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