Insight-Sogang-Univ/insight-13th
May 2025 – May 2025
1 Month active
Languages Used
Jupyter NotebookPythonSQL
Technical Skills
Association Rule MiningBag of WordsCatBoostClassificationCollaborative FilteringCosine Similarity
gaeun011225
PROFILE
Gaeun011225
Gaeun Lee developed four end-to-end features for the Insight-Sogang-Univ/insight-13th repository, focusing on data-driven employee analytics, personalized recommendations, and transactional analysis. Leveraging Python, Jupyter Notebook, and PyTorch, Gaeun implemented machine learning workflows for employee data, including preprocessing, multiple classifiers, and ensemble methods with hyperparameter tuning. She built a collaborative filtering system for personalized chicken dish recommendations and applied association rule mining to analyze customer transactions, extracting actionable insights for sales strategies. Additionally, she completed deep learning and NLP coursework, demonstrating proficiency in model training, evaluation, and text vectorization. The work reflects strong depth in applied machine learning techniques.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
4
Lines of code
21,811
Activity Months1
Your Network
1029 people
Work History
May 2025
6 Commits • 4 Features
May 1, 2025May 2025: Delivered four end-to-end features in Insight-Sogang-Univ/insight-13th, focusing on data-driven employee insights, personalized recommendations, and transactional analytics, with coursework-driven demonstrations of ML/NLP capabilities. Business value: enhanced hiring analytics, improved customer experience through recommendations, and data-backed sales insights. Technical achievements include end-to-end ML pipelines, collaborative filtering, association rule mining, PyTorch-based MNIST, and NLP preprocessing with BoW/TF-IDF and Word2Vec; demonstrated ensemble methods and hyperparameter tuning for robust models. No critical bugs fixed this month.
6 Commits • 4 Features
May 1, 2025May 2025: Delivered four end-to-end features in Insight-Sogang-Univ/insight-13th, focusing on data-driven employee insights, personalized recommendations, and transactional analytics, with coursework-driven demonstrations of ML/NLP capabilities. Business value: enhanced hiring analytics, improved customer experience through recommendations, and data-backed sales insights. Technical achievements include end-to-end ML pipelines, collaborative filtering, association rule mining, PyTorch-based MNIST, and NLP preprocessing with BoW/TF-IDF and Word2Vec; demonstrated ensemble methods and hyperparameter tuning for robust models. No critical bugs fixed this month.
May 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability76.6%
Architecture76.6%
Performance71.6%
AI Usage33.4%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
Association Rule MiningBag of WordsCatBoostClassificationCollaborative FilteringCosine SimilarityData AnalysisData PreprocessingData ScienceDeep LearningEnsemble MethodsFeature EngineeringHyperparameter TuningLightGBMMLxtend
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