Insight-Sogang-Univ/insight-14th
Nov 2025 – Nov 2025
1 Month active
Languages Used
Pythonpython
Technical Skills
JupyterKerasPythonTensorFlowdata analysisdata science
greentreeswbang
PROFILE
Greentreeswbang
During November 2025, Greentree contributed to the Insight-Sogang-Univ/insight-14th repository by developing two core features focused on data science and deep learning education. Greentree implemented ensemble regression models in Python using scikit-learn to improve the accuracy and robustness of California housing price predictions, addressing the challenge of reliable real estate estimation. Additionally, Greentree created a suite of Jupyter notebooks and assignments covering deep learning fundamentals, including CNN-based image classification and RNN/LSTM/Seq2Seq architectures, leveraging Keras and TensorFlow. The work demonstrated solid technical depth, providing both practical modeling solutions and accessible educational resources without introducing major bugs during the period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
19,028
Activity Months1
Your Network
10 people
Work History
November 2025
5 Commits • 2 Features
Nov 1, 2025November 2025 (2025-11) — Insight-Sogang-Univ/insight-14th: Delivered two key features and strengthened learning resources with no major bugs fixed this month. Key features delivered: 1) Ensemble Regression for California Housing Price Prediction — implemented ensemble regression models to improve accuracy and robustness of housing-price estimates on the California dataset (commit 196f8393656ae7e7a825a560eca4eece9712a3b1). 2) Educational Deep Learning Notebooks and Assignments — created and published a curated set of notebooks and assignments covering deep learning fundamentals, CNN-based image classification (Cats vs Dogs, Pokemon), and RNN/LSTM/SEQ2SEQ concepts (commits: 961a7fb8fdb60520585e196f92622ffbf7bf9d95; 1cbaf4e0be100e616bbd2291533ff7fbd01c76d7; 3995cd0301f033a4fd8708f01469cf70b62e6e70; f6a5f703a5cf5648466c4c618e04d7362eda23e4). Major bugs fixed: none reported. Overall impact and accomplishments: these efforts advance data science capability, enabling better pricing decisions through ensemble modeling and accelerating student learning with ready-to-run material. Technologies/skills demonstrated: Python, ensemble methods (bagging/boosting/stacking), scikit-learn, Jupyter notebooks, deep learning foundations (CNNs, RNN/LSTM/Seq2Seq), Git/version control.
5 Commits • 2 Features
Nov 1, 2025November 2025 (2025-11) — Insight-Sogang-Univ/insight-14th: Delivered two key features and strengthened learning resources with no major bugs fixed this month. Key features delivered: 1) Ensemble Regression for California Housing Price Prediction — implemented ensemble regression models to improve accuracy and robustness of housing-price estimates on the California dataset (commit 196f8393656ae7e7a825a560eca4eece9712a3b1). 2) Educational Deep Learning Notebooks and Assignments — created and published a curated set of notebooks and assignments covering deep learning fundamentals, CNN-based image classification (Cats vs Dogs, Pokemon), and RNN/LSTM/SEQ2SEQ concepts (commits: 961a7fb8fdb60520585e196f92622ffbf7bf9d95; 1cbaf4e0be100e616bbd2291533ff7fbd01c76d7; 3995cd0301f033a4fd8708f01469cf70b62e6e70; f6a5f703a5cf5648466c4c618e04d7362eda23e4). Major bugs fixed: none reported. Overall impact and accomplishments: these efforts advance data science capability, enabling better pricing decisions through ensemble modeling and accelerating student learning with ready-to-run material. Technologies/skills demonstrated: Python, ensemble methods (bagging/boosting/stacking), scikit-learn, Jupyter notebooks, deep learning foundations (CNNs, RNN/LSTM/Seq2Seq), Git/version control.
November 2025
Activity
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Quality Metrics
Correctness96.0%
Maintainability88.0%
Architecture96.0%
Performance88.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Pythonpython
Technical Skills
JupyterKerasPythonTensorFlowdata analysisdata sciencedeep learningmachine learningpandasscikit-learn
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