Insight-Sogang-Univ/insight-13th
Mar 2025 – Jun 2025
4 Months active
Languages Used
CSVJSONJupyter NotebookPython
Technical Skills
Control FlowData AnalysisData EngineeringData ManipulationData PreprocessingData Structures
i-am-jsung
PROFILE
I-am-jsung
Nadak Kim developed a suite of end-to-end analytics and machine learning features for the Insight-Sogang-Univ/insight-13th repository, focusing on practical data science workflows and reproducible research. Over four months, Nadak built educational Python notebooks covering data preprocessing, feature engineering, and model evaluation, and delivered predictive models such as multi-class classifiers and LSTM-based time series forecasters. Using technologies like Python, Pandas, and PyTorch, Nadak implemented solutions for tasks including regression analysis, collaborative filtering, and association rule mining. The work demonstrated depth in both classic and modern machine learning, providing actionable insights and robust pipelines for data-driven decision support and reporting.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
11
Lines of code
150,377
Activity Months4
Your Network
1029 people
Work History
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025: Delivered two end-to-end analytics features for Insight-Sogang-Univ/insight-13th that unlock data-driven planning for NumVehicles and electricity consumption. Implemented an OLS regression visualization with a full regression summary for NumVehicles, enabling clear visibility of predicted vs actual values and model statistics for reporting. Built an LSTM-based time-series forecasting pipeline for electricity consumption, including data preprocessing, model definition, training, and evaluation. These enhancements provide stakeholders with actionable forecasts and reporting artifacts, reducing manual analysis time and improving planning accuracy.
2 Commits • 2 Features
Jun 1, 2025June 2025: Delivered two end-to-end analytics features for Insight-Sogang-Univ/insight-13th that unlock data-driven planning for NumVehicles and electricity consumption. Implemented an OLS regression visualization with a full regression summary for NumVehicles, enabling clear visibility of predicted vs actual values and model statistics for reporting. Built an LSTM-based time-series forecasting pipeline for electricity consumption, including data preprocessing, model definition, training, and evaluation. These enhancements provide stakeholders with actionable forecasts and reporting artifacts, reducing manual analysis time and improving planning accuracy.
June 2025
May 2025
6 Commits • 4 Features
May 1, 2025May 2025 monthly summary for Insight-Sogang-Univ/insight-13th. Delivered a diverse set of end-to-end ML, data mining, and NLP capabilities across enterprise data, with a clear focus on business value, actionable insights, and scalable pipelines. The month’s work demonstrates cross-domain proficiency from predictive modeling to customer behavior analytics and language processing.
May 2025
6 Commits • 4 Features
May 1, 2025May 2025 monthly summary for Insight-Sogang-Univ/insight-13th. Delivered a diverse set of end-to-end ML, data mining, and NLP capabilities across enterprise data, with a clear focus on business value, actionable insights, and scalable pipelines. The month’s work demonstrates cross-domain proficiency from predictive modeling to customer behavior analytics and language processing.
April 2025
2 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for Insight-Sogang-Univ/insight-13th: Key features delivered include implementing a Passenger Embarkation Port Prediction model and adding a Baseball Batter Data CSV to enable analytics. Major bugs fixed: none reported. Overall impact: established data-driven modeling capabilities and expanded analytics datasets, enabling upcoming experiments and insights for passenger flow and baseball analytics. Technologies/skills demonstrated: Python, scikit-learn, data preprocessing, feature engineering, model evaluation for multi-class classification, CSV data integration, and Git-based version control.
2 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for Insight-Sogang-Univ/insight-13th: Key features delivered include implementing a Passenger Embarkation Port Prediction model and adding a Baseball Batter Data CSV to enable analytics. Major bugs fixed: none reported. Overall impact: established data-driven modeling capabilities and expanded analytics datasets, enabling upcoming experiments and insights for passenger flow and baseball analytics. Technologies/skills demonstrated: Python, scikit-learn, data preprocessing, feature engineering, model evaluation for multi-class classification, CSV data integration, and Git-based version control.
April 2025
March 2025
7 Commits • 3 Features
Mar 1, 2025March 2025 (2025-03) – Insight-Sogang-Univ/insight-13th Key features delivered: - Educational Pandas and Python Notebooks Bundle: covers Pandas fundamentals (Series and DataFrames), data manipulation (sorting, transforming, merging, grouping), and introductory data analysis workflows for learning and pre-assignment practice. - Penguin Dataset Preprocessing and Visualization: preprocessing with label encoding and one-hot encoding; visualization of data before and after outlier removal. - House Price Data Resources and Modeling Tutorials: notebooks for data loading, preprocessing, feature engineering, and model evaluation using Linear Regression and statsmodels; includes correlation analysis and VIF-based feature selection. Major bugs fixed: - No major bugs documented for this period. Focused on feature development and content delivery. Overall impact and accomplishments: - Strengthened the data science learning pathway with end-to-end notebooks covering data loading, cleaning, feature engineering, modeling, and evaluation; improved reproducibility and learner engagement through session-based commits; added practical, hands-on material to support coursework and pre-assignment practice. Technologies/skills demonstrated: - Pandas, Python notebooks, data wrangling (sorting, transforming, merging, grouping); categorical encoding (label encoding, one-hot encoding); data visualization; outlier handling; feature engineering; model evaluation with Linear Regression and statsmodels; correlation analysis and VIF; notebook-based teaching materials; version-controlled workflow across multiple commits.
March 2025
7 Commits • 3 Features
Mar 1, 2025March 2025 (2025-03) – Insight-Sogang-Univ/insight-13th Key features delivered: - Educational Pandas and Python Notebooks Bundle: covers Pandas fundamentals (Series and DataFrames), data manipulation (sorting, transforming, merging, grouping), and introductory data analysis workflows for learning and pre-assignment practice. - Penguin Dataset Preprocessing and Visualization: preprocessing with label encoding and one-hot encoding; visualization of data before and after outlier removal. - House Price Data Resources and Modeling Tutorials: notebooks for data loading, preprocessing, feature engineering, and model evaluation using Linear Regression and statsmodels; includes correlation analysis and VIF-based feature selection. Major bugs fixed: - No major bugs documented for this period. Focused on feature development and content delivery. Overall impact and accomplishments: - Strengthened the data science learning pathway with end-to-end notebooks covering data loading, cleaning, feature engineering, modeling, and evaluation; improved reproducibility and learner engagement through session-based commits; added practical, hands-on material to support coursework and pre-assignment practice. Technologies/skills demonstrated: - Pandas, Python notebooks, data wrangling (sorting, transforming, merging, grouping); categorical encoding (label encoding, one-hot encoding); data visualization; outlier handling; feature engineering; model evaluation with Linear Regression and statsmodels; correlation analysis and VIF; notebook-based teaching materials; version-controlled workflow across multiple commits.
Activity
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Quality Metrics
Correctness83.0%
Maintainability81.2%
Architecture80.6%
Performance76.6%
AI Usage28.8%
Skills & Technologies
Programming Languages
CSVJSONJupyter NotebookPython
Technical Skills
Association Rule MiningBERTCatBoostClassification ModelsCollaborative FilteringControl FlowData AnalysisData EngineeringData ManipulationData PreprocessingData StructuresData VisualizationDeep LearningEnsemble MethodsExploratory Data Analysis (EDA)
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