HUFS-DAT/2024-2_Seminar
Nov 2024 – Nov 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData PreprocessingData VisualizationDimensionality ReductionFile Management
Chaehwanny
PROFILE
Chaehwanny
Contributed four data analytics notebooks and performed repository cleanup for HUFS-DAT/2024-2_Seminar, focusing on exploratory analysis and project maintainability. Developed a Jupyter Notebook applying PCA-based feature extraction and dimensionality reduction to the Fashion MNIST dataset, enabling effective visualization of high-dimensional data. Delivered an end-to-end baseball statistics analysis notebook, covering data loading, preprocessing, exploratory data analysis, and feature importance assessment. Added a coffee consumption survey analysis notebook, emphasizing data cleaning and initial exploration. Removed an outdated regression notebook to streamline the repository. Leveraged Python, Pandas, and Scikit-learn throughout, demonstrating depth in data preprocessing, visualization, and dimensionality reduction techniques.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
8,028
Activity Months1
Your Network
6 peopleWork History
November 2024
5 Commits • 4 Features
Nov 1, 2024November 2024 monthly summary for HUFS-DAT/2024-2_Seminar: Delivered four data analytics notebooks and repository cleanup that enhance exploratory analysis, reproducibility, and maintainability. Key notebooks cover PCA-based feature extraction on Fashion MNIST, end-to-end baseball performance analysis with feature importance, coffee habits survey analysis, and removal of an obsolete regression notebook to streamline the project.
5 Commits • 4 Features
Nov 1, 2024November 2024 monthly summary for HUFS-DAT/2024-2_Seminar: Delivered four data analytics notebooks and repository cleanup that enhance exploratory analysis, reproducibility, and maintainability. Key notebooks cover PCA-based feature extraction on Fashion MNIST, end-to-end baseball performance analysis with feature importance, coffee habits survey analysis, and removal of an obsolete regression notebook to streamline the project.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture72.0%
Performance72.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData PreprocessingData VisualizationDimensionality ReductionFile ManagementJupyter NotebookMachine LearningMatplotlibNumPyPCAPandasPyTorchPythonScikit-learn
Repositories Contributed To
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