![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2Fbe82e26b5aeda5c8e081dc4b644a16e3%2FStats%20(7).jpg?generation=1772058410261148&alt=media)

# 📓 Notebook Submission Requirements

All participants are required to submit a **Kaggle Notebook** that meets production-level standards and executes seamlessly within the Kaggle environment.

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## ⚙️ Execution Requirements

The notebook must:

* ✅ Execute **end-to-end without manual intervention**
* ✅ Run from top to bottom without errors
* ✅ Generate the final `submission.csv` programmatically
* ✅ Be fully reproducible inside the Kaggle runtime

> No hidden steps. No external dependencies. No manual edits.

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## 🏗 Engineering & Pipeline Standards

Your notebook must include:

### 🔹 Structured Preprocessing Pipeline

* Proper categorical encoding
* Missing value handling
* Feature transformations
* Clear separation between training and inference logic

Pipeline-based implementation (e.g., `sklearn Pipeline`) is strongly recommended.

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### 🔹 Cross-Validation Strategy

* Use **Stratified K-Fold** (or equivalent stratified validation)
* Avoid single train/validation splits
* Ensure no data leakage between folds

Cross-validation must be clearly implemented and documented.

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### 🔹 Deterministic Results

* Fix all relevant random seeds:

  * NumPy
  * Python random
  * Model-level seeds
* Ensure reproducible outputs across multiple runs

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### 🔹 Validation Metrics

The notebook must:

* Compute and display **ROC AUC**
* Show cross-validation mean and standard deviation
* Clearly distinguish between validation and final predictions

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### 🔹 Feature Importance / Interpretability

Include at least one of the following:

* Feature importance visualization (e.g., tree-based models)
* SHAP analysis
* Permutation importance
* Model coefficient analysis (if applicable)

The goal is to demonstrate **model transparency and reasoning**, not just performance.

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## 🔁 Reproducibility Requirement

The entire workflow must be:

* Self-contained
* Deterministic
* Fully executable within Kaggle

Any notebook that fails to reproduce results will be disqualified.

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