HUFS-DAT/2024-2_Seminar
Nov 2024 – Nov 2024
1 Month active
Languages Used
CSSHTMLJupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData PreprocessingData VisualizationExploratory Data Analysis (EDA)Jupyter Notebook
Roosuri0214
PROFILE
Roosuri0214
During November 2024, Gygns Lee developed three data analytics features for the HUFS-DAT/2024-2_Seminar repository, focusing on customer insights, anomaly detection, and marketing research. They implemented K-Means clustering with elbow-based selection and visualization using Python, Pandas, and scikit-learn, enabling more robust customer segmentation analysis. Gygns also created a Coffee Consumption Analysis Notebook to examine consumption patterns by demographic factors, supporting data-driven product positioning. Additionally, they built a leak type classification model with LightGBM, incorporating preprocessing and performance reporting to enhance anomaly detection. The work demonstrated depth in data preprocessing, exploratory analysis, and machine learning model evaluation within Jupyter Notebooks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
4,021
Activity Months1
Your Network
9 peopleSame Organization
@hufs.ac.kr3
Work History
November 2024
3 Commits • 3 Features
Nov 1, 2024November 2024: Delivered three data analytics features in HUFS-DAT/2024-2_Seminar to strengthen customer insights, anomaly detection, and marketing research. Implemented K-Means Clustering Analysis and Evaluation (elbow-based selection, visualizations, evaluation framework) enabling broader clustering comparisons and better customer segmentation decisions. Launched Coffee Consumption Analysis Notebook to explore consumption patterns across age, gender, and income for product positioning and market research. Added Leak Type Classification with LightGBM, including preprocessing, model training, and performance reporting to improve anomaly detection and troubleshooting. No major bugs fixed this month. Technologies demonstrated: Python data science stack (scikit-learn, LightGBM), notebook-based analytics, data preprocessing and visualization. Commit references: 0fad0c41a0a22769f8a19e2e98c4a1f676a1baba; aa33fed5b7fcdd4938dcefb6919779415013babe; 5e1944f06fac419337b408ae2108555050d7b394.
3 Commits • 3 Features
Nov 1, 2024November 2024: Delivered three data analytics features in HUFS-DAT/2024-2_Seminar to strengthen customer insights, anomaly detection, and marketing research. Implemented K-Means Clustering Analysis and Evaluation (elbow-based selection, visualizations, evaluation framework) enabling broader clustering comparisons and better customer segmentation decisions. Launched Coffee Consumption Analysis Notebook to explore consumption patterns across age, gender, and income for product positioning and market research. Added Leak Type Classification with LightGBM, including preprocessing, model training, and performance reporting to improve anomaly detection and troubleshooting. No major bugs fixed this month. Technologies demonstrated: Python data science stack (scikit-learn, LightGBM), notebook-based analytics, data preprocessing and visualization. Commit references: 0fad0c41a0a22769f8a19e2e98c4a1f676a1baba; aa33fed5b7fcdd4938dcefb6919779415013babe; 5e1944f06fac419337b408ae2108555050d7b394.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture73.4%
Performance66.6%
AI Usage40.0%
Skills & Technologies
Programming Languages
CSSHTMLJupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData PreprocessingData VisualizationExploratory Data Analysis (EDA)Jupyter NotebookK-Means ClusteringLightGBMMachine LearningMatplotlibPandasPythonScikit-learnSeaborn
Repositories Contributed To
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