General
PromptBeginner5 minmarkdown
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General
PromptBeginner5 minmarkdown
df.dropna(subset=[TARGET_COL]
axis=0
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General
PromptBeginner5 minmarkdown
DISGUISED_NULLS = [?
N/A
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General
PromptBeginner5 minmarkdown
df.replace(DISGUISED_NULLS
np.nan
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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
│ Linear Models (LogReg
LinearReg
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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
│ → Creates: col_was_missing = 1 if NaN
else 0 │
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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
**For each flagged column
fill in this analysis card:**
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General
PromptBeginner5 minmarkdown
│ │ Signs: Missingness correlates with OTHER columns
│
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General
PromptBeginner5 minmarkdown
missing_report = missing_report.sort_values('Missing_%'
ascending=False)
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General
PromptBeginner5 minmarkdown
DISGUISED_NULLS = [?
N/A
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General
PromptBeginner5 minmarkdown
df.replace(DISGUISED_NULLS
np.nan
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General
PromptBeginner5 minmarkdown
- e.g.
CustomerID must be unique and non-null
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General
PromptBeginner5 minmarkdown
- e.g.
Age cannot be 0 or negative
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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
- Watch for: ?
N/A
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General
PromptBeginner5 minmarkdown
- Boolean → binary flags (0/1
True/False)
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General
PromptBeginner5 minmarkdown
MODEL_TYPE = [e.g.
XGBoost / LinearRegression / Neural Network]
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General
PromptBeginner5 minmarkdown
TARGET_COL = [e.g.
price]
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Data
PromptBeginner5 minmarkdown
Your job is to analyze `DATA()` and produce a fully reproducible
explainable missing value treatment plan.
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