Insight-Sogang-Univ/insight-13th
Mar 2025 – Jun 2025
4 Months active
Languages Used
Jupyter NotebookPythonSQL
Technical Skills
Data AnalysisData PreprocessingData ScienceDecision TreesExploratory Data AnalysisJupyter Notebook
hangyul00
PROFILE
Hangyul00
Over four months, Jihwan Lee developed and maintained a suite of data science and machine learning notebooks for the Insight-Sogang-Univ/insight-13th repository. He delivered structured educational content and production-grade analytics pipelines, including time series forecasting with ARIMA and LSTM, classification and clustering models for business prediction tasks, and collaborative filtering for recommendation systems. Using Python, Jupyter Notebook, and Scikit-learn, he emphasized reproducible workflows, robust data preprocessing, and clear model evaluation. His work demonstrated depth in both foundational and advanced topics, providing reusable, well-organized materials that improved onboarding, supported research, and enabled data-driven decision making without reported defects.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
21Total
Bugs
0
Commits
21
Features
13
Lines of code
94,582
Activity Months4
Your Network
1029 people
Work History
June 2025
4 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for Insight-Sogang-Univ/insight-13th. Delivery focused on enhancing time-series forecasting capabilities through three feature streams, reinforcing data-driven decision making and analytical reliability. No major bugs were reported this month. Impact: enabled more accurate energy consumption and climate forecasts, with reusable notebooks and clear commit traceability to support reproducibility and future iterations. Technologies demonstrated include STL-based decomposition, stationarity testing (ADF and KPSS), differencing pipelines, LSTM-based forecasting with sliding-window data and early stopping, and ARIMA-based forecasting notebooks.
4 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for Insight-Sogang-Univ/insight-13th. Delivery focused on enhancing time-series forecasting capabilities through three feature streams, reinforcing data-driven decision making and analytical reliability. No major bugs were reported this month. Impact: enabled more accurate energy consumption and climate forecasts, with reusable notebooks and clear commit traceability to support reproducibility and future iterations. Technologies demonstrated include STL-based decomposition, stationarity testing (ADF and KPSS), differencing pipelines, LSTM-based forecasting with sliding-window data and early stopping, and ARIMA-based forecasting notebooks.
June 2025
May 2025
8 Commits • 4 Features
May 1, 2025Monthly work summary for 2025-05 focused on delivering feature-rich notebooks and ML exploration across Insight-Sogang-Univ/insight-13th. Delivered four major notebooks encompassing model evaluation, collaborative filtering recommendations, association rule mining for transaction analysis, and NLP/DL exploration. Emphasis on data-driven business insights, model readiness for HR decisions, personalized recommendations, and scalable ML/NLP workflows. No explicit defects reported; commit activity shows task completion and iterative refinements across all notebooks.
May 2025
8 Commits • 4 Features
May 1, 2025Monthly work summary for 2025-05 focused on delivering feature-rich notebooks and ML exploration across Insight-Sogang-Univ/insight-13th. Delivered four major notebooks encompassing model evaluation, collaborative filtering recommendations, association rule mining for transaction analysis, and NLP/DL exploration. Emphasis on data-driven business insights, model readiness for HR decisions, personalized recommendations, and scalable ML/NLP workflows. No explicit defects reported; commit activity shows task completion and iterative refinements across all notebooks.
April 2025
2 Commits • 2 Features
Apr 1, 2025April 2025 — Insight-Sogang-Univ/insight-13th: Delivered ML-driven predictive and analytical features with a clear business value. No major bugs were reported this month. Overall impact includes a strengthened foundation for research-grade analytics and reproducible ML workflows applicable to port prediction and player performance clustering. Technologies demonstrated include supervised classification, clustering theory, and dimensionality reduction, along with robust data loading, cleaning, feature engineering, and scaling.
2 Commits • 2 Features
Apr 1, 2025April 2025 — Insight-Sogang-Univ/insight-13th: Delivered ML-driven predictive and analytical features with a clear business value. No major bugs were reported this month. Overall impact includes a strengthened foundation for research-grade analytics and reproducible ML workflows applicable to port prediction and player performance clustering. Technologies demonstrated include supervised classification, clustering theory, and dimensionality reduction, along with robust data loading, cleaning, feature engineering, and scaling.
April 2025
March 2025
7 Commits • 4 Features
Mar 1, 2025March 2025 monthly work summary focusing on delivering a structured, self-contained learning path in Insight-Sogang-Univ/insight-13th. Key effort centered on producing and organizing educational notebooks across Python fundamentals, Pandas basics, a comprehensive Pandas guide, and ML (Sessions 3-4), as well as scaffolding coursework for Sessions 0-2. This work enhances learner onboarding, accelerates hands-on practice, and improves maintainability of course materials.
March 2025
7 Commits • 4 Features
Mar 1, 2025March 2025 monthly work summary focusing on delivering a structured, self-contained learning path in Insight-Sogang-Univ/insight-13th. Key effort centered on producing and organizing educational notebooks across Python fundamentals, Pandas basics, a comprehensive Pandas guide, and ML (Sessions 3-4), as well as scaffolding coursework for Sessions 0-2. This work enhances learner onboarding, accelerates hands-on practice, and improves maintainability of course materials.
Activity
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Quality Metrics
Correctness79.0%
Maintainability79.0%
Architecture79.0%
Performance74.2%
AI Usage33.4%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
ADF TestARIMAARIMA ModelingAssociation Rule MiningBERTCatBoostClassificationClusteringCollaborative FilteringData AnalysisData CleaningData LoadingData PreprocessingData ScienceData Visualization
Repositories Contributed To
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