General
PromptBeginner5 minmarkdownQuality: 22
│ → Creates: colwasmissing = 1 if NaN
else 0 │
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General
PromptBeginner5 minmarkdownQuality: 22
│ Linear Models (LogReg
LinearReg
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General
PromptBeginner5 minmarkdownQuality: 22
│ → Use max_iter=10
random_state=42 for reproducibility │
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General
PromptBeginner5 minmarkdownQuality: 22
→ df.dropna(subset=[TARGET_COL]
inplace=True)
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General
PromptBeginner5 minmarkdownQuality: 22
For each flagged column
fill in this analysis card:
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General
PromptBeginner5 minmarkdownQuality: 22
For each column with missing values
evaluate all three branches simultaneously:
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General
PromptBeginner5 minmarkdownQuality: 22
│ │ Signs: Missingness correlates with OTHER columns
│
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General
PromptBeginner5 minmarkdownQuality: 22
missingreport = missingreport.sortvalues('Missing%'
ascending=False)
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General
PromptBeginner5 minmarkdownQuality: 22
df.replace(DISGUISED_NULLS
np.nan
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General
PromptBeginner5 minmarkdownQuality: 22
DISGUISED_NULLS = [?
N/A
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General
PromptBeginner5 minmarkdownQuality: 22
- Watch for: ?
N/A
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General
PromptBeginner5 minmarkdownQuality: 22
- e.g.
Age cannot be 0 or negative
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General
PromptBeginner5 minmarkdownQuality: 22
- Boolean → binary flags (0/1
True/False)
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General
PromptBeginner5 minmarkdownQuality: 22
MODEL_TYPE = [e.g.
XGBoost / LinearRegression / Neural Network]
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General
PromptBeginner5 minmarkdownQuality: 22
TARGET_COL = [e.g.
price]
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Data
PromptBeginner5 minmarkdownQuality: 26
Your job is to analyze DATA() and produce a fully reproducible
explainable missing value treatment plan.
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Coding & Debugging
PromptBeginner5 minmarkdownQuality: 26
You are a Senior Data Scientist and ML Pipeline Engineer specializing in data quality
feature engineering
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Productivity
PromptBeginner5 minmarkdownQuality: 22
ML_TASK = [e.g.
Binary Classification]
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Coding & Debugging
PromptBeginner5 minmarkdownQuality: 26
Provide structured updates to PROGRESS.md to keep the context usage under 40%. Do not make direct co...
accurate
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General
PromptBeginner5 minmarkdownQuality: 27
| PROMPT() | This master template — governs all reasoning
rules
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General
PromptBeginner5 minmarkdownQuality: 22
MISSING VALUES HANDLER
PROMPT() — UNIVERSAL MISSING VALUES HANDLER
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General
PromptBeginner5 minmarkdownQuality: 22
- Modular References: Linking to secondary markdowns (like PRDs
sprint_todo.md
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Productivity
PromptBeginner5 minmarkdownQuality: 22
- Task Specificity: Documenting exact file paths
target line numbers
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General
PromptBeginner5 minmarkdownQuality: 22
- Architectural Constraints: Ensuring that strict structural rules
DevSecOps guidelines
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