Insight-Sogang-Univ/insight-13th
Mar 2025 – Jun 2025
4 Months active
Languages Used
Jupyter NotebookPythonSQL
Technical Skills
Data AnalysisData PreprocessingData ScienceData VisualizationExploratory Data Analysis (EDA)Feature Engineering
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PROFILE
Mingsaaa
Belita developed a suite of data science and machine learning resources for the Insight-Sogang-Univ/insight-13th repository, focusing on practical workflows and educational content. Over four months, she built Jupyter Notebook-based pipelines for data preprocessing, feature engineering, and model evaluation, covering topics such as classification, clustering, association rule mining, and time series forecasting. Her work included implementing ML models with scikit-learn and PyTorch, developing an LSTM-based forecasting toolkit, and integrating NLP assignments using BERT and GPT. By emphasizing reproducibility and hands-on learning, Belita delivered a robust, modular platform that accelerates onboarding and supports scalable, data-driven experimentation for new practitioners.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
21Total
Bugs
0
Commits
21
Features
7
Lines of code
51,303
Activity Months4
Your Network
28 people
Work History
June 2025
3 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for the Insight-Sogang-Univ/insight-13th project. Focused on delivering a comprehensive Time Series Analysis and Forecasting Toolkit, with end-to-end data handling, educational content, and practical forecasting capabilities. Major activity centered on feature delivery and documentation, with no reported bug fixes this month.
3 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for the Insight-Sogang-Univ/insight-13th project. Focused on delivering a comprehensive Time Series Analysis and Forecasting Toolkit, with end-to-end data handling, educational content, and practical forecasting capabilities. Major activity centered on feature delivery and documentation, with no reported bug fixes this month.
June 2025
May 2025
6 Commits • 3 Features
May 1, 2025May 2025 monthly summary for Insight-Sogang-Univ/insight-13th: Delivered a cohesive analytics and learning platform that accelerates data-driven decision making and practical ML skills. Key features delivered include an integrated data analytics workflow combining ML modeling (classification with multiple models, preprocessing, hyperparameter tuning) and association rule mining (Apriori/FP-Growth) to derive actionable insights from employee and transactional data; a PyTorch-based MLP for MNIST digit classification with end-to-end pipeline (data loading, preprocessing, training, evaluation, visualization); and educational ML/NLP notebooks and assignments covering BERT, GPT, LangChain, and RAG to reinforce theoretical concepts with hands-on practice. No major bugs reported this month; focus was on delivering robust features and high-quality educational content. Technologies demonstrated include PyTorch, ML modeling and hyperparameter tuning, association rule mining (Apriori/FP-Growth), and NLP tooling (BERT, GPT, LangChain, RAG). Major business impact includes faster insight generation, improved predictive capabilities, and richer training resources for practitioners.
May 2025
6 Commits • 3 Features
May 1, 2025May 2025 monthly summary for Insight-Sogang-Univ/insight-13th: Delivered a cohesive analytics and learning platform that accelerates data-driven decision making and practical ML skills. Key features delivered include an integrated data analytics workflow combining ML modeling (classification with multiple models, preprocessing, hyperparameter tuning) and association rule mining (Apriori/FP-Growth) to derive actionable insights from employee and transactional data; a PyTorch-based MLP for MNIST digit classification with end-to-end pipeline (data loading, preprocessing, training, evaluation, visualization); and educational ML/NLP notebooks and assignments covering BERT, GPT, LangChain, and RAG to reinforce theoretical concepts with hands-on practice. No major bugs reported this month; focus was on delivering robust features and high-quality educational content. Technologies demonstrated include PyTorch, ML modeling and hyperparameter tuning, association rule mining (Apriori/FP-Growth), and NLP tooling (BERT, GPT, LangChain, RAG). Major business impact includes faster insight generation, improved predictive capabilities, and richer training resources for practitioners.
April 2025
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 — Focused on delivering practical ML learning assets and a reproducible data analysis workflow for Insight-Sogang-Univ/insight-13th. Key outcomes include two Jupyter notebooks covering introductory classification and clustering concepts, plus a data analysis pipeline notebook implementing data loading, preprocessing, feature engineering, scaling, PCA, and application of K-Means with the elbow method. No major bugs reported this month. Business value: accelerates student onboarding, standardizes experiment workflows, and enables rapid prototyping for ML tasks. Technologies demonstrated: Python, Jupyter, scikit-learn, PCA, clustering, and data wrangling.
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 — Focused on delivering practical ML learning assets and a reproducible data analysis workflow for Insight-Sogang-Univ/insight-13th. Key outcomes include two Jupyter notebooks covering introductory classification and clustering concepts, plus a data analysis pipeline notebook implementing data loading, preprocessing, feature engineering, scaling, PCA, and application of K-Means with the elbow method. No major bugs reported this month. Business value: accelerates student onboarding, standardizes experiment workflows, and enables rapid prototyping for ML tasks. Technologies demonstrated: Python, Jupyter, scikit-learn, PCA, clustering, and data wrangling.
April 2025
March 2025
10 Commits • 2 Features
Mar 1, 2025March 2025 focused on delivering a structured learning resource within Insight-Sogang-Univ/insight-13th through two notebook-driven features and consolidating content for scalable onboarding. Key outcomes include the creation and maintenance of Pandas fundamentals notebooks with Titanic dataset tutorials, plus Notebook-based data analysis practice covering linear regression, preprocessing, feature engineering, and evaluation. The work established a reusable, reproducible workflow and improved accessibility for learners and contributors.
March 2025
10 Commits • 2 Features
Mar 1, 2025March 2025 focused on delivering a structured learning resource within Insight-Sogang-Univ/insight-13th through two notebook-driven features and consolidating content for scalable onboarding. Key outcomes include the creation and maintenance of Pandas fundamentals notebooks with Titanic dataset tutorials, plus Notebook-based data analysis practice covering linear regression, preprocessing, feature engineering, and evaluation. The work established a reusable, reproducible workflow and improved accessibility for learners and contributors.
Activity
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Quality Metrics
Correctness74.2%
Maintainability73.4%
Architecture71.4%
Performance66.6%
AI Usage28.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
ARIMAAssociation Rule MiningBERTCatBoostClassificationClusteringData AnalysisData PreprocessingData ScienceData VisualizationDeep LearningEnsemble MethodsExploratory Data Analysis (EDA)Feature EngineeringFile Management
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