H6WU6R/DSA3101-Group-4
Mar 2025 – Apr 2025
2 Months active
Languages Used
PythonMarkdown
Technical Skills
Customer Churn PredictionData PreprocessingData VisualizationFile ManagementImbalanced-learnMachine Learning
Dellayfd
PROFILE
Dellayfd
Over a two-month period, contributed to the H6WU6R/DSA3101-Group-4 repository by developing a reusable customer churn prediction workflow and delivering comprehensive project documentation. The work unified data preprocessing, feature engineering, and model training using Python, Pandas, and Scikit-learn, with a focus on handling imbalanced data through SMOTE and merging datasets for a single churn view. Refactored the repository structure to improve maintainability and deployment readiness, removing deprecated scripts and organizing code under Customer Retention Strategies. Enhanced onboarding and reproducibility by documenting the end-to-end data preparation pipeline, supporting scalable analytics and consistent machine learning workflows for customer retention.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
2
Lines of code
451
Activity Months2
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Work History
April 2025
12 Commits • 1 Features
Apr 1, 2025April 2025 Monthly Summary for H6WU6R/DSA3101-Group-4: Delivered a comprehensive Bank Customer Churn Dataset Documentation and Setup Guide. Primary focus was on documenting the end-to-end data preparation pipeline (data cleaning by dropping irrelevant columns, feature engineering (Income_bin), dataset merging, encoding of categorical variables, and SMOTE balancing) and enhancements to documentation structure, formatting, and navigation to support reproducible preparation for customer lifetime value prediction. No major code defects fixed this month; impact centered on developer onboarding, reproducibility, and readiness for scalable data pipelines. Business value: faster onboarding, consistent preprocessing for CLV models, and improved collaboration.
12 Commits • 1 Features
Apr 1, 2025April 2025 Monthly Summary for H6WU6R/DSA3101-Group-4: Delivered a comprehensive Bank Customer Churn Dataset Documentation and Setup Guide. Primary focus was on documenting the end-to-end data preparation pipeline (data cleaning by dropping irrelevant columns, feature engineering (Income_bin), dataset merging, encoding of categorical variables, and SMOTE balancing) and enhancements to documentation structure, formatting, and navigation to support reproducible preparation for customer lifetime value prediction. No major code defects fixed this month; impact centered on developer onboarding, reproducibility, and readiness for scalable data pipelines. Business value: faster onboarding, consistent preprocessing for CLV models, and improved collaboration.
April 2025
March 2025
4 Commits • 1 Features
Mar 1, 2025In March 2025, delivered a reusable churn-prediction workflow for H6WU6R/DSA3101-Group-4 and reorganized repository structure to boost maintainability and deployment readiness. The work unified data preprocessing, model training (Logistic Regression, Random Forest, Gradient Boosting), and evaluation, with improved handling of imbalanced data via SMOTE and data merging for a single churn view. Deprecated scripts were removed and files reorganized under Customer Retention Strategies to streamline future development and analytics deployments. Business value: enables faster, data-driven retention decisions and scalable analytics for customer churn.
March 2025
4 Commits • 1 Features
Mar 1, 2025In March 2025, delivered a reusable churn-prediction workflow for H6WU6R/DSA3101-Group-4 and reorganized repository structure to boost maintainability and deployment readiness. The work unified data preprocessing, model training (Logistic Regression, Random Forest, Gradient Boosting), and evaluation, with improved handling of imbalanced data via SMOTE and data merging for a single churn view. Deprecated scripts were removed and files reorganized under Customer Retention Strategies to streamline future development and analytics deployments. Business value: enables faster, data-driven retention decisions and scalable analytics for customer churn.
Activity
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Quality Metrics
Correctness96.2%
Maintainability96.2%
Architecture95.0%
Performance95.0%
AI Usage21.2%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
Customer Churn PredictionData PreprocessingData VisualizationDocumentationFeature EngineeringFile ManagementImbalanced-learnMachine LearningModel EvaluationModel TrainingNumPyPandasRefactoringScikit-learn
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