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
# STEP 12 — MICE / IterativeImputer (most powerful
use when needed)
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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
X_train
X_test
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General
PromptBeginner5 minmarkdown
df.dropna(subset=[TARGET_COL]
axis=0
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General
PromptBeginner5 minmarkdown
X
y
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General
PromptBeginner5 minmarkdown
df.replace(DISGUISED_NULLS
np.nan
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General
PromptBeginner5 minmarkdown
DISGUISED_NULLS = [?
N/A
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General
PromptBeginner5 minmarkdown
│ Linear Models (LogReg
LinearReg
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General
PromptBeginner5 minmarkdown
│ Tree-based (XGBoost
LightGBM
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General
PromptBeginner5 minmarkdown
│ SVM
KNN Classifier: │
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General
PromptBeginner5 minmarkdown
│ → Creates: col_was_missing = 1 if NaN
else 0 │
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General
PromptBeginner5 minmarkdown
→ df.dropna(subset=[TARGET_COL]
inplace=True)
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General
PromptBeginner5 minmarkdown
│ → Use max_iter=10
random_state=42 for reproducibility │
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General
PromptBeginner5 minmarkdown
│ │ Signs: Missingness correlates with OTHER columns
│
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General
PromptBeginner5 minmarkdown
**For each flagged column
fill in this analysis card:**
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General
PromptBeginner5 minmarkdown
For **each column** with missing values
evaluate all three branches simultaneously:
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General
PromptBeginner5 minmarkdown
missing_report = missing_report.sort_values('Missing_%'
ascending=False)
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General
PromptBeginner5 minmarkdown
df.replace(DISGUISED_NULLS
np.nan
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General
PromptBeginner5 minmarkdown
DISGUISED_NULLS = [?
N/A
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General
PromptBeginner5 minmarkdown
- e.g.
Price is the target — rows missing it are unusable
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General
PromptBeginner5 minmarkdown
- e.g.
Age cannot be 0 or negative
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General
PromptBeginner5 minmarkdown
- e.g.
CustomerID must be unique and non-null
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