During two months on the Yihe-Harry/DSA3101-Group-Project repository, this developer delivered end-to-end deployment of churn, ROI, and customer preference prediction models, integrating pre-trained XGBoost and K-means artifacts into a Streamlit dashboard with Flask APIs. They enhanced data loading by supporting Excel files via openpyxl and improved model transparency by surfacing RMSE metrics. Their work included reorganizing the project’s directory structure for maintainability and onboarding, standardizing asset management, and refining dashboard input precision for marketing metrics. Using Python, Pandas, and Docker, they addressed both backend integration and data engineering challenges, demonstrating depth in model deployment and scalable code organization.
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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