Quant-of-Renmin-University/Quant_RUC
Sep 2025 – Dec 2025
4 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
AutomationBERTData AnalysisData ProcessingData VisualizationDocument Generation
huangama448
PROFILE
Huangama448
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
18Total
Bugs
0
Commits
18
Features
8
Lines of code
21,175
Activity Months4
Your Network
83 people
Work History
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for Quant_RUC: Established foundational repository scaffolding and project structure with initial files, enabling rapid future feature development. No code changes executed this month; the groundwork supports onboarding, planning, and future CI/CD integration. Two early commits added the scaffolding (630bdf95154d0f2925ad2c4e105b04812f301583 and 36fec7220ec3b00dff6f93b4b5496c2f9f099ac5).
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for Quant_RUC: Established foundational repository scaffolding and project structure with initial files, enabling rapid future feature development. No code changes executed this month; the groundwork supports onboarding, planning, and future CI/CD integration. Two early commits added the scaffolding (630bdf95154d0f2925ad2c4e105b04812f301583 and 36fec7220ec3b00dff6f93b4b5496c2f9f099ac5).
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 performance summary for Quant_RUC. Delivered an end-to-end Rental Price Prediction Pipeline Notebook in the Quant-of-Renmin-University/Quant_RUC repository, establishing a reproducible data processing and modeling workflow. The notebook integrates data cleaning, feature engineering, K-Means clustering for market segmentation, and multiple regression models to predict rental prices. No major bugs reported; focus on ensuring setup and data pipeline consistency. This work provides a foundation for pricing optimization and scenario analysis, enabling faster experimentation and stakeholder-ready deliverables. Demonstrated technologies include Python, Jupyter, pandas, scikit-learn, and ML workflow practices within a notebook.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 performance summary for Quant_RUC. Delivered an end-to-end Rental Price Prediction Pipeline Notebook in the Quant-of-Renmin-University/Quant_RUC repository, establishing a reproducible data processing and modeling workflow. The notebook integrates data cleaning, feature engineering, K-Means clustering for market segmentation, and multiple regression models to predict rental prices. No major bugs reported; focus on ensuring setup and data pipeline consistency. This work provides a foundation for pricing optimization and scenario analysis, enabling faster experimentation and stakeholder-ready deliverables. Demonstrated technologies include Python, Jupyter, pandas, scikit-learn, and ML workflow practices within a notebook.
October 2025
10 Commits • 5 Features
Oct 1, 2025Monthly summary for 2025-10: Key features delivered and business impact across the Quant_RUC repository. Major bugs fixed: none reported this month. Overall impact: established end-to-end data automation and analytics capabilities with scalable notebooks and templates, enabling faster decision-making and reproducible workflows. Technologies/skills demonstrated: Python, data engineering, web scraping, ML models (KMeans, Linear, Ridge, Lasso, Random Forest), NLP with BERT, Jupyter notebooks, template-based document automation, and PDF generation.
10 Commits • 5 Features
Oct 1, 2025Monthly summary for 2025-10: Key features delivered and business impact across the Quant_RUC repository. Major bugs fixed: none reported this month. Overall impact: established end-to-end data automation and analytics capabilities with scalable notebooks and templates, enabling faster decision-making and reproducible workflows. Technologies/skills demonstrated: Python, data engineering, web scraping, ML models (KMeans, Linear, Ridge, Lasso, Random Forest), NLP with BERT, Jupyter notebooks, template-based document automation, and PDF generation.
October 2025
September 2025
5 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Quant_RUC monthly performance summary. Focused on assets lifecycle hygiene for Homework 2023200086 in the Finance module; implemented initialization, asset addition, and cleanup to keep the repository organized. Deliverables: - Created a placeholder file for 2023200086 and added image HW2_2023200086.jpg with standardized naming. - Established asset lifecycle practices for the homework materials, ensuring consistent future asset handling. Cleanup and maintenance: - Removed outdated and redundant assets to reduce repository clutter and storage usage, improving maintainability. Impact and value: - Enhanced repo hygiene, faster asset discovery for QA, and a scalable foundation for ongoing asset governance in Finance homework materials. Technologies/skills demonstrated: - Git-based workflow with atomic commits (create, delete, upload), asset management, and file/directory operations; adherence to naming conventions and lifecycle policies.
September 2025
5 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Quant_RUC monthly performance summary. Focused on assets lifecycle hygiene for Homework 2023200086 in the Finance module; implemented initialization, asset addition, and cleanup to keep the repository organized. Deliverables: - Created a placeholder file for 2023200086 and added image HW2_2023200086.jpg with standardized naming. - Established asset lifecycle practices for the homework materials, ensuring consistent future asset handling. Cleanup and maintenance: - Removed outdated and redundant assets to reduce repository clutter and storage usage, improving maintainability. Impact and value: - Enhanced repo hygiene, faster asset discovery for QA, and a scalable foundation for ongoing asset governance in Finance homework materials. Technologies/skills demonstrated: - Git-based workflow with atomic commits (create, delete, upload), asset management, and file/directory operations; adherence to naming conventions and lifecycle policies.
Activity
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Quality Metrics
Correctness93.4%
Maintainability92.2%
Architecture92.2%
Performance91.2%
AI Usage26.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
AutomationBERTData AnalysisData ProcessingData VisualizationDocument GenerationJupyter NotebookK-Means clusteringMatplotlibNumPyPandasRegular ExpressionsSeleniumStatistical ModelingStatsmodels
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline