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
During November 2024, Lotte developed end-to-end machine learning notebook templates for the HUFS-DAT/2024-2_Seminar repository, focusing on baseball analytics and general data preprocessing. Lotte designed reusable Jupyter Notebook workflows for data loading, cleaning, feature engineering, and model training, including a logistic regression prototype for forecasting player performance. The work emphasized reproducibility and scalability, enabling faster experimentation across multiple datasets such as coffee demographics. Leveraging Python, Pandas, and Scikit-learn, Lotte established a foundation of shareable assets and templates that streamline onboarding and validation cycles. The engineering approach demonstrated depth in workflow design and practical application of core data science tools.
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 people
Work 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
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