HUFS-DAT/2024-2_Seminar
Nov 2024 – Dec 2024
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData PreprocessingData VisualizationDimensionality ReductionMachine LearningMatplotlib
Jiyoon-B
PROFILE
Jiyoon-b
Jiyoon Kim developed robust data analysis and preprocessing solutions in the HUFS-DAT/2024-2_Seminar repository, focusing on reproducibility and analytical depth. They built a PCA-based Jupyter Notebook for Fashion MNIST, applying multi-component dimensionality reduction and visualizing results with Matplotlib and Seaborn to enhance interpretability. Kim also improved reliability in baseball analytics notebooks by correcting file paths and standardizing preprocessing for metrics like woba. In December, they delivered an end-to-end data preparation pipeline for the model1 dataset, implementing missing value checks, correlation analysis, and scaling using Pandas and Scikit-learn, which streamlined data quality and accelerated experimental workflows.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
5,394
Activity Months2
Your Network
6 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Month: 2024-12 | Repository: HUFS-DAT/2024-2_Seminar | Focus: Feature delivery in data preprocessing notebook for model1 dataset with end-to-end data prep pipeline.
1 Commits • 1 Features
Dec 1, 2024Month: 2024-12 | Repository: HUFS-DAT/2024-2_Seminar | Focus: Feature delivery in data preprocessing notebook for model1 dataset with end-to-end data prep pipeline.
December 2024
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 performance highlights focused on delivering a reproducible, analytics-ready environment in HUFS-DAT/2024-2_Seminar. Key features delivered a PCA-based image data notebook for Fashion MNIST with multi-component PCA, variance analysis, reconstruction error, and label-colored 2D visualization, enabling deeper dimensionality reduction experiments. Major fixes addressed notebook reliability in Baseball Analytics by correcting file paths, execution counts, and preprocessing/model parameters for metrics like woba and exit_velocity_avg. Collectively, these efforts improved data exploration capabilities, reliability, and onboarding for analysts, with tangible business value in faster insights and more robust analytics pipelines.
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 performance highlights focused on delivering a reproducible, analytics-ready environment in HUFS-DAT/2024-2_Seminar. Key features delivered a PCA-based image data notebook for Fashion MNIST with multi-component PCA, variance analysis, reconstruction error, and label-colored 2D visualization, enabling deeper dimensionality reduction experiments. Major fixes addressed notebook reliability in Baseball Analytics by correcting file paths, execution counts, and preprocessing/model parameters for metrics like woba and exit_velocity_avg. Collectively, these efforts improved data exploration capabilities, reliability, and onboarding for analysts, with tangible business value in faster insights and more robust analytics pipelines.
Activity
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Quality Metrics
Correctness66.6%
Maintainability66.6%
Architecture66.6%
Performance66.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData PreprocessingData VisualizationDimensionality ReductionMachine LearningMatplotlibPCAPandasPythonScikit-learnSeaborn
Repositories Contributed To
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