Insight-Sogang-Univ/insight-13th
Mar 2025 – Jun 2025
4 Months active
Languages Used
Jupyter NotebookPythonSQL
Technical Skills
Data AnalysisData PreprocessingData VisualizationExploratory Data AnalysisFeature EngineeringJupyter Notebook
il-ho-kim
PROFILE
Il-ho-kim
Ilho Kim developed a suite of end-to-end machine learning and data science notebooks for the Insight-Sogang-Univ/insight-13th repository, focusing on practical education and reusable workflows. Over four months, he delivered features such as regression modeling, supervised and unsupervised learning pipelines, and a unified experimentation suite spanning NLP, recommendation systems, and time series forecasting. His work emphasized reproducibility and traceability, using Python, Jupyter Notebook, and PyTorch to implement robust data preprocessing, feature engineering, and model evaluation. By consolidating experiments and standardizing pipelines, Ilho enabled scalable, auditable workflows that support both business insights and effective onboarding for new learners.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
8
Lines of code
98,233
Activity Months4
Your Network
57 people
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focusing on key business value and technical achievements for Insight-Sogang-Univ/insight-13th. Key feature delivered: Electricity Consumption Forecasting with LSTM. Implemented end-to-end time-series forecasting pipeline with data loading, differencing preprocessing, PyTorch Dataset/DataLoader, LSTM model, training with early stopping, and multi-metric evaluation. No major bugs reported in scope. This work provides a scalable forecasting capability to support capacity planning and energy management decisions, enabling proactive resource allocation and improved accuracy of demand forecasts. Technologies demonstrated include PyTorch, time-series preprocessing, end-to-end ML pipeline, and Git-based collaboration.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focusing on key business value and technical achievements for Insight-Sogang-Univ/insight-13th. Key feature delivered: Electricity Consumption Forecasting with LSTM. Implemented end-to-end time-series forecasting pipeline with data loading, differencing preprocessing, PyTorch Dataset/DataLoader, LSTM model, training with early stopping, and multi-metric evaluation. No major bugs reported in scope. This work provides a scalable forecasting capability to support capacity planning and energy management decisions, enabling proactive resource allocation and improved accuracy of demand forecasts. Technologies demonstrated include PyTorch, time-series preprocessing, end-to-end ML pipeline, and Git-based collaboration.
June 2025
May 2025
7 Commits • 2 Features
May 1, 2025May 2025: Delivered a unified AI/ML Experimentation Suite for Insight-Sogang-Univ/insight-13th, consolidating experiments across multiple domains including employee leave prediction, collaborative filtering for recommendations, MNIST classification, NLP preprocessing and modeling (TF-IDF, Word2Vec, BERT, GPT), and time series forecasting. The suite provides a single, reusable workflow for data ingestion, model training, evaluation, and results tracking across domains, reducing setup time, improving reproducibility, and accelerating cross-domain insights. A clear, session-based commit history enhances traceability and governance. Administrative Submission Placeholder commit demonstrates process discipline with no code changes.
May 2025
7 Commits • 2 Features
May 1, 2025May 2025: Delivered a unified AI/ML Experimentation Suite for Insight-Sogang-Univ/insight-13th, consolidating experiments across multiple domains including employee leave prediction, collaborative filtering for recommendations, MNIST classification, NLP preprocessing and modeling (TF-IDF, Word2Vec, BERT, GPT), and time series forecasting. The suite provides a single, reusable workflow for data ingestion, model training, evaluation, and results tracking across domains, reducing setup time, improving reproducibility, and accelerating cross-domain insights. A clear, session-based commit history enhances traceability and governance. Administrative Submission Placeholder commit demonstrates process discipline with no code changes.
April 2025
2 Commits • 1 Features
Apr 1, 2025In April 2025, delivered end-to-end ML education notebooks for Insight-Sogang-Univ/insight-13th, covering supervised and unsupervised learning pipelines, with a Session 7 notebook for data loading, preprocessing, feature engineering, scaling, PCA, and K-Means for player categorization. The work focuses on practical ML education content using the Titanic dataset for supervised learning and a suite of clustering algorithms for unsupervised learning, establishing a reusable educational resource and a baseline for future experiments.
2 Commits • 1 Features
Apr 1, 2025In April 2025, delivered end-to-end ML education notebooks for Insight-Sogang-Univ/insight-13th, covering supervised and unsupervised learning pipelines, with a Session 7 notebook for data loading, preprocessing, feature engineering, scaling, PCA, and K-Means for player categorization. The work focuses on practical ML education content using the Titanic dataset for supervised learning and a suite of clustering algorithms for unsupervised learning, establishing a reusable educational resource and a baseline for future experiments.
April 2025
March 2025
6 Commits • 4 Features
Mar 1, 20252025-03 Monthly Summary for Insight-Sogang-Univ/insight-13th. Focused on delivering classroom-ready data science notebooks that demonstrate end-to-end Python-based data analysis, from data loading and preprocessing to modeling and visualization. Primary activity centered on feature delivery with clear traceability via commits. No explicit bug fixes documented for this period; emphasis was on building reusable learning materials and foundational techniques with measurable business value.
March 2025
6 Commits • 4 Features
Mar 1, 20252025-03 Monthly Summary for Insight-Sogang-Univ/insight-13th. Focused on delivering classroom-ready data science notebooks that demonstrate end-to-end Python-based data analysis, from data loading and preprocessing to modeling and visualization. Primary activity centered on feature delivery with clear traceability via commits. No explicit bug fixes documented for this period; emphasis was on building reusable learning materials and foundational techniques with measurable business value.
Activity
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Quality Metrics
Correctness76.8%
Maintainability75.0%
Architecture75.6%
Performance66.2%
AI Usage27.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
BERTCatBoostClassificationClusteringCollaborative FilteringData AnalysisData PreprocessingData VisualizationDeep LearningEnsemble MethodsExploratory Data AnalysisFeature EngineeringGPTHugging FaceJupyter Notebook
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