Insight-Sogang-Univ/insight-13th
May 2025 – Jun 2025
2 Months active
Languages Used
Jupyter NotebookPythonSQL
Technical Skills
Association Rule MiningCatBoostClassificationCollaborative FilteringData AnalysisData Preprocessing
hyunji1122
PROFILE
Hyunji1122
Over a two-month period, HJ Lee developed a suite of data science features for the Insight-Sogang-Univ/insight-13th repository, focusing on both machine learning and time series analytics. Lee built reproducible Jupyter notebooks covering ensemble classification, collaborative filtering, and market basket analysis, leveraging Python, PyTorch, and Scikit-learn to deliver business insights and assignment-ready artifacts. In June, Lee expanded the project with end-to-end time series workflows, implementing STL decomposition, stationarity testing with ADF and KPSS, and LSTM-based forecasting for energy and climate datasets. The work demonstrated depth in model evaluation, data preprocessing, and clear documentation for reproducibility and scalability.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
8
Lines of code
66,942
Activity Months2
Your Network
1029 people
Work History
June 2025
3 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for Insight-Sogang-Univ/insight-13th: Focused on delivering scalable time-series analytics and forecasting capabilities. No major bugs reported this month; changes centered on feature delivery and experimental notebooks to enable data-driven decision making for energy usage and climate data. Impact highlights include enabling end-to-end time-series analysis, production-style forecasting readiness, and exploratory data analysis for additional datasets, with an emphasis on reproducibility and documentation.
3 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for Insight-Sogang-Univ/insight-13th: Focused on delivering scalable time-series analytics and forecasting capabilities. No major bugs reported this month; changes centered on feature delivery and experimental notebooks to enable data-driven decision making for energy usage and climate data. Impact highlights include enabling end-to-end time-series analysis, production-style forecasting readiness, and exploratory data analysis for additional datasets, with an emphasis on reproducibility and documentation.
June 2025
May 2025
10 Commits • 7 Features
May 1, 2025Month 2025-05 — Insight-Sogang-Univ/insight-13th: Delivered a cohesive set of notebooks and experiments spanning ensemble methods, collaborative filtering, market basket analysis, MNIST classification, and Korean NLP preprocessing. Established reproducible workflows, improved model evaluation, and produced artifacts suitable for assignments and business insights. Key outcomes include tuned ensemble experiments with RandomForestClassifier to boost classification accuracy, MNIST MLP achieving ~89% accuracy, collaborative filtering with MAE/MSE/RMSE evaluation, market basket analytics via Apriori/FP-Growth, and TF-IDF based Korean NLP preprocessing. The work delivers tangible business value through better recommendations, insights into basket data, and ready-to-share research artifacts.
May 2025
10 Commits • 7 Features
May 1, 2025Month 2025-05 — Insight-Sogang-Univ/insight-13th: Delivered a cohesive set of notebooks and experiments spanning ensemble methods, collaborative filtering, market basket analysis, MNIST classification, and Korean NLP preprocessing. Established reproducible workflows, improved model evaluation, and produced artifacts suitable for assignments and business insights. Key outcomes include tuned ensemble experiments with RandomForestClassifier to boost classification accuracy, MNIST MLP achieving ~89% accuracy, collaborative filtering with MAE/MSE/RMSE evaluation, market basket analytics via Apriori/FP-Growth, and TF-IDF based Korean NLP preprocessing. The work delivers tangible business value through better recommendations, insights into basket data, and ready-to-share research artifacts.
Activity
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Quality Metrics
Correctness67.6%
Maintainability66.2%
Architecture63.8%
Performance60.0%
AI Usage23.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
ADF TestARIMA ModelingAssociation Rule MiningCatBoostClassificationCollaborative FilteringData AnalysisData DecompositionData PreprocessingData VisualizationDeep LearningDifferencingEnsemble MethodsExploratory Data AnalysisHyperparameter Tuning
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