
During January 2025, Sihyeon Kim developed two end-to-end analytics notebooks in the halley1116/2025_DA_study repository, focusing on data-driven pricing and churn analysis. Sihyeon applied Python, Pandas, and Scikit-learn to implement robust data loading, preprocessing, and exploratory data analysis workflows. The insurance analytics notebook explored BMI and smoking impacts on charges, using both linear and ridge regression for pricing models. The churn analysis notebook established a foundation for customer churn insights through data merging and visualization. Each notebook emphasized reproducibility and clear documentation, providing structured, traceable assets that support rapid experimentation and informed business decisions in analytics projects.

January 2025 (2025-01): Delivered two end-to-end analytics notebooks in halley1116/2025_DA_study to enable data-driven pricing and churn insights. Implemented robust data loading, preprocessing, EDA, and modeling foundations with clear traceability, setting up reproducible assets for rapid experimentation and informed business decisions.
January 2025 (2025-01): Delivered two end-to-end analytics notebooks in halley1116/2025_DA_study to enable data-driven pricing and churn insights. Implemented robust data loading, preprocessing, EDA, and modeling foundations with clear traceability, setting up reproducible assets for rapid experimentation and informed business decisions.
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