HUFS-DAT/2024-2_Seminar
Nov 2024 – Nov 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData PreprocessingData VisualizationJupyter NotebookMachine Learning
kimdh123000
PROFILE
Kimdh123000
Developed end-to-end machine learning notebook templates for the HUFS-DAT/2024-2_Seminar repository, focusing on baseball analytics and generalized data preprocessing. Leveraged Python, Jupyter Notebook, and libraries such as Pandas and Scikit-learn to implement reusable workflows for data loading, cleaning, feature engineering, model training, and visualization. Delivered a logistic regression prototype for forecasting player performance and created preprocessing assets applicable to multiple datasets, including coffee demographics. Emphasized reproducibility and scalability by establishing modular notebook templates, enabling faster experimentation and onboarding for future projects. All contributions were additive, with no critical bugs reported, reflecting a focus on robust, shareable data science assets.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
2
Lines of code
26,362
Activity Months1
Your Network
6 peopleWork History
November 2024
6 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for HUFS-DAT/2024-2_Seminar: Focused on delivering end-to-end ML notebook templates for baseball analytics and generalized preprocessing. Implemented reusable workflows, improved reproducibility, and created a logistic regression model prototype for player performance forecasting. No critical bugs reported this month; all changes were additive and aimed at scalable, shareable data science assets.
6 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for HUFS-DAT/2024-2_Seminar: Focused on delivering end-to-end ML notebook templates for baseball analytics and generalized preprocessing. Implemented reusable workflows, improved reproducibility, and created a logistic regression model prototype for player performance forecasting. No critical bugs reported this month; all changes were additive and aimed at scalable, shareable data science assets.
November 2024
Activity
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Quality Metrics
Correctness56.6%
Maintainability56.6%
Architecture56.6%
Performance56.6%
AI Usage30.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData PreprocessingData VisualizationJupyter NotebookMachine LearningMatplotlibNumPyPandasScikit-learnSeaborn
Repositories Contributed To
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