New project: Bank Subscription Prediction #206
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Pull Request Overview
This PR introduces a new ZenML project for Bank Subscription Prediction using XGBoost, with components for data loading, cleaning, preprocessing, splitting, model training, evaluation, and configuration management.
- Added utility functions for model operations
- Implemented multiple ZenML pipeline steps and a training pipeline definition
- Included YAML configuration files and extended project documentation
Reviewed Changes
Copilot reviewed 21 out of 21 changed files in this pull request and generated 3 comments.
Show a summary per file
| File | Description |
|---|---|
| bank_subscription_prediction/utils/model_utils.py | Added utility functions for calculating scale_pos_weight and retrieving feature importances |
| bank_subscription_prediction/steps/model_trainer.py | Created a step for training an XGBoost classifier with feature selection |
| bank_subscription_prediction/steps/model_evaluator.py | Developed model evaluation steps with interactive HTML visualizations |
| bank_subscription_prediction/steps/data_splitter.py | Added a step to split data into training and test sets with stratification support |
| bank_subscription_prediction/steps/data_preprocessor.py | Implemented data preprocessing by handling categorical variables and unnecessary columns |
| bank_subscription_prediction/steps/data_loader.py | Introduced a data loader that downloads data automatically if not found locally |
| bank_subscription_prediction/steps/data_cleaner.py | Added data cleaning to drop missing values and adjust column types |
| bank_subscription_prediction/run.py | Created a main script to run the entire training pipeline using click CLI |
| bank_subscription_prediction/configs/ | Provided YAML configurations for baseline, more trees, and deeper trees experiments |
| bank_subscription_prediction/README.md | Updated documentation detailing project setup, structure, and usage |
| pos_count = class_counts[1] | ||
| return neg_count / pos_count | ||
| else: | ||
| print("Warning: Could not calculate scale_pos_weight. Using default value 1.") |
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Consider replacing the print statement with a logging call (e.g., logger.warning) for more consistent and configurable warning output in production.
Suggested change
| print("Warning: Could not calculate scale_pos_weight. Using default value 1.") | |
| logging.warning("Could not calculate scale_pos_weight. Using default value 1.") |
| if stratify_col and stratify_col in df.columns: | ||
| stratify_data = df[stratify_col] | ||
| elif stratify_col: | ||
| print(f"Warning: Stratification column '{stratify_col}' not found. Proceeding without stratification.") |
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Replace the print statement with a logging warning to ensure consistency with the rest of the project’s logging practices.
Suggested change
| print(f"Warning: Stratification column '{stratify_col}' not found. Proceeding without stratification.") | |
| logging.warning(f"Stratification column '{stratify_col}' not found. Proceeding without stratification.") |
| y_test=y_test | ||
| ) | ||
|
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| print("Bank subscription training pipeline completed.") No newline at end of file |
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Use a logging statement (e.g., logger.info) instead of print to maintain consistent logging practices across the pipeline.
Suggested change
| print("Bank subscription training pipeline completed.") | |
| logger.info("Bank subscription training pipeline completed.") |
strickvl
requested changes
May 16, 2025
Co-authored-by: Alex Strick van Linschoten <[email protected]>
…or the project.</footer>
strickvl
reviewed
May 16, 2025
| # Install dependencies with uv and cache optimization | ||
| RUN --mount=type=cache,target=/root/.cache/uv \ | ||
| uv pip install --system \ | ||
| "zenml[server]>=0.50.0" \ |
strickvl
approved these changes
May 16, 2025
strickvl
requested changes
May 16, 2025
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strickvl
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Wait you need also to update the README.md
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