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
During November 2024, Coghks worked on the HUFS-DAT/2024-2_Seminar repository, delivering four Jupyter Notebooks focused on data analytics and project maintainability. They developed a PCA-based feature extraction notebook for Fashion MNIST, applying dimensionality reduction and visualization using Python, Pandas, and Scikit-learn. Another notebook provided end-to-end analysis of baseball statistics, including data preprocessing and feature importance evaluation. Coghks also analyzed coffee consumption survey data, emphasizing data cleaning and exploratory analysis. To streamline the project, they removed an outdated regression notebook, improving repository structure. The work demonstrated depth in data analysis, preprocessing, and visualization, with careful attention to reproducibility.
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 people
Work 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
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