General
PromptBeginner5 minmarkdownQuality: 27
| Categorical
high cardinality | Fill with 'Unknown' category |
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General
PromptBeginner5 minmarkdownQuality: 27
| Categorical
low cardinality | Mode imputation |
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General
PromptBeginner5 minmarkdownQuality: 27
| Numerical
time-series | Forward fill / Interpolation |
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General
PromptBeginner5 minmarkdownQuality: 22
→ Check for disguised missing values (?
N/A
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General
PromptBeginner5 minmarkdownQuality: 27
| Numerical
skewed dist | Median imputation |
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General
PromptBeginner5 minmarkdownQuality: 27
| Numerical
symmetric dist | Mean imputation |
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General
PromptBeginner5 minmarkdownQuality: 22
[Column] — Reason: [e.g.
72% missing
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General
PromptBeginner5 minmarkdownQuality: 22
- Any disguised nulls found (?
N/A
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General
PromptBeginner5 minmarkdownQuality: 22
After completing Phases 1–4
deliver this exact report:
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General
PromptBeginner5 minmarkdownQuality: 22
assert remaining_test.sum() == 0
fTest still has missing:\n{remainingtest[remainingtest > 0]}
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General
PromptBeginner5 minmarkdownQuality: 22
impiter = IterativeImputer(maxiter=10
random_state=42)
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General
PromptBeginner5 minmarkdownQuality: 26
assert remaining_train.sum() == 0
fTrain still has missing:\n{remainingtrain[remainingtrain > 0]}
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General
PromptBeginner5 minmarkdownQuality: 22
Xtest = Xtest.drop(columns=drop_cols
errors='ignore')
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General
PromptBeginner5 minmarkdownQuality: 22
STEP 12 — MICE / IterativeImputer (most powerful
use when needed)
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General
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Xtrain = Xtrain.drop(columns=drop_cols
errors='ignore')
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PromptBeginner5 minmarkdownQuality: 22
X_train
X_test
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General
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X
y
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General
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df.dropna(subset=[TARGET_COL]
axis=0
1
General
PromptBeginner5 minmarkdownQuality: 22
DISGUISED_NULLS = [?
N/A
0
General
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df.replace(DISGUISED_NULLS
np.nan
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Education
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from sklearn.impute import SimpleImputer
KNNImputer
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General
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│ Tree-based (XGBoost
LightGBM
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General
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│ SVM
KNN Classifier: │
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General
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│ Linear Models (LogReg
LinearReg
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