Month: 2025-11 — Focused on delivering a Transformer-based chatbot feature for Insight-Sogang-Univ/insight-14th. Implemented an end-to-end Transformer architecture (encoder/decoder with attention, positional encoding, and masking) to enable robust conversational capabilities. Progress anchored by PR [session04] 과제 pr (commit 7ecc3c49bed793311bf3c1355aae9fc2109985f9). No major bugs reported within the scope of this work. This delivers business value by enabling scalable, high-quality dialogue interactions and lays the groundwork for future evaluation and fine-tuning, with strong traceability and modular design.

June 2025 monthly summary for Insight-Sogang-Univ/insight-13th. Focused on delivering a time-series forecasting notebook for Seoul's monthly average temperature using ARIMA. The work produced a complete notebook with data loading, preprocessing, visualization, and stationarity testing, and uncovered a strong seasonal component that informs model design.

May 2025 monthly summary for Insight-Sogang-Univ/insight-13th: Delivered five data science features across ML modeling, market analytics, NLP, computer vision, and statistical evaluation. Focused on practical business value through predictive HR analytics, marketing insights, sentiment analysis deployment considerations, and robust modeling visuals. No major bugs reported this month; continued building reusable experimentation pipelines and data-driven decision support.

April 2025: Focused feature delivery in Insight-13th. Key features delivered include two educational ML notebooks (clustering theory with evaluation methods; classification theory with Titanic multi-class task) and an applied notebook covering data preprocessing, feature engineering, PCA, and K-Means clustering on baseball data with elbow method for cluster count. No major bugs fixed this month. Overall impact: created scalable, classroom-ready ML training material enabling faster skill development and reproducibility, contributing to education and internal knowledge transfer. Technologies/skills demonstrated: clustering algorithms (hierarchical, k-means, DBSCAN, Gaussian Mixture Models), classification models, data preprocessing, feature engineering, PCA, elbow method, Jupyter notebooks, Python data science stack, and documentation.

March 2025: Delivered a structured notebook learning path in Insight-Sogang-Univ/insight-13th, spanning introductory Python/Pandas, comprehensive exploratory data analysis (EDA), advanced data manipulation, regression modeling with practical evaluation, and a foundations-focused ML track for house-price projects. All sessions emphasized reproducible workflows, clear data notes, and practical exercises to accelerate downstream data work. No major bugs were reported this month; the focus was on feature delivery, notebook quality, and establishing a scalable onboarding pathway. Business value includes faster onboarding of data practitioners, reusable analysis templates, and readiness for data-driven product insights across teams.