Contributed to the Yihe-Harry/DSA3101-Group-Project by delivering end-to-end deployment of machine learning models for customer churn, ROI prediction, and customer segmentation. Focused on robust backend integration and API development using Python, Flask, and Streamlit, the work included refactoring repository structure for maintainability and onboarding efficiency. Enhanced data loading with Excel and openpyxl compatibility, and implemented pre-trained XGBoost and K-means models for campaign optimization and clustering. Improved dashboard usability by refining parameter input precision and adding transparency to prediction results. Emphasized code organization, version control discipline, and scalable asset management to support evolving data science and web development needs.
April 2025 monthly summary for Yihe-Harry/DSA3101-Group-Project. Delivered end-to-end churn model deployment with data loading enhancements (xlsx/openpyxl) and compatibility fixes; launched ROI Prediction, Customer Preference Prediction, and Customer Clustering features with pre-trained artifacts and API endpoints; added Prediction Results Transparency and refined dashboard parameter input precision for campaign metrics. These efforts enabled faster deployments, improved data-driven decision-making for churn reduction and marketing optimization, and stronger governance of model predictions.
April 2025 monthly summary for Yihe-Harry/DSA3101-Group-Project. Delivered end-to-end churn model deployment with data loading enhancements (xlsx/openpyxl) and compatibility fixes; launched ROI Prediction, Customer Preference Prediction, and Customer Clustering features with pre-trained artifacts and API endpoints; added Prediction Results Transparency and refined dashboard parameter input precision for campaign metrics. These efforts enabled faster deployments, improved data-driven decision-making for churn reduction and marketing optimization, and stronger governance of model predictions.
Month: 2025-03 – Delivered refactor-oriented improvements focused on project structure, directory organization, and UI assets to enable faster onboarding, clearer component separation, and smoother UI iteration. The changes emphasize maintainability, readability, and scalable asset handling as the project evolves.
Month: 2025-03 – Delivered refactor-oriented improvements focused on project structure, directory organization, and UI assets to enable faster onboarding, clearer component separation, and smoother UI iteration. The changes emphasize maintainability, readability, and scalable asset handling as the project evolves.
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