Quant-of-Renmin-University/Quant_RUC
Sep 2025 – Dec 2025
4 Months active
Languages Used
Jupyter NotebookNonePythonUnknown
Technical Skills
AutomationBeautifulSoupCSV HandlingData AnalysisData CleaningData Extraction
Bubble628
PROFILE
Bubble628
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
30Total
Bugs
0
Commits
30
Features
7
Lines of code
28,894
Activity Months4
Your Network
83 people
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Laid the foundation for Quant_RUC by establishing project scaffolding and repository setup, enabling rapid future development and onboarding. This groundwork emphasizes long-term business value by supporting scalable feature work and consistent project structure for easier maintenance and collaboration.
1 Commits • 1 Features
Dec 1, 2025December 2025: Laid the foundation for Quant_RUC by establishing project scaffolding and repository setup, enabling rapid future development and onboarding. This groundwork emphasizes long-term business value by supporting scalable feature work and consistent project structure for easier maintenance and collaboration.
December 2025
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025: Delivered new real estate data processing notebooks and ML feature engineering for price and rent analysis in Quant_RUC. Implemented robust preprocessing (cleaning, missing-value handling, categorical encoding) and expanded feature engineering to include linear, nonlinear, and dummy-variable features to accelerate ML readiness and improve model performance. No major bugs reported this period. This work improves data quality, accelerates pricing/rent insights, and strengthens data-driven decision-making for real estate investments. Technologies used include Python, Jupyter notebooks, and standard data-science tooling. Commits demonstrate concrete delivery.
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025: Delivered new real estate data processing notebooks and ML feature engineering for price and rent analysis in Quant_RUC. Implemented robust preprocessing (cleaning, missing-value handling, categorical encoding) and expanded feature engineering to include linear, nonlinear, and dummy-variable features to accelerate ML readiness and improve model performance. No major bugs reported this period. This work improves data quality, accelerates pricing/rent insights, and strengthens data-driven decision-making for real estate investments. Technologies used include Python, Jupyter notebooks, and standard data-science tooling. Commits demonstrate concrete delivery.
October 2025
17 Commits • 4 Features
Oct 1, 2025October 2025 (2025-10) monthly summary for Quant_RUC. Focused on delivering end-to-end features, establishing data-driven tooling, and improving project maintainability in the Quant-of-Renmin-University/Quant_RUC repository.
17 Commits • 4 Features
Oct 1, 2025October 2025 (2025-10) monthly summary for Quant_RUC. Focused on delivering end-to-end features, establishing data-driven tooling, and improving project maintainability in the Quant-of-Renmin-University/Quant_RUC repository.
October 2025
September 2025
10 Commits • 1 Features
Sep 1, 2025September 2025 — Quant-of-Renmin-University/Quant_RUC: Delivered a robust asset lifecycle for Finance Homework 2023200130, covering materials setup and cleanup. Implemented initialization of placeholder assets, image uploads, and systematic removals to ensure clean, reproducible material provisioning. Established idempotent create/delete sequences to prevent orphaned files and directory clutter, resulting in improved storage hygiene and faster readiness for subsequent coursework deployments. This work lays groundwork for future feature expansions and enhances overall maintainability of the repository.
September 2025
10 Commits • 1 Features
Sep 1, 2025September 2025 — Quant-of-Renmin-University/Quant_RUC: Delivered a robust asset lifecycle for Finance Homework 2023200130, covering materials setup and cleanup. Implemented initialization of placeholder assets, image uploads, and systematic removals to ensure clean, reproducible material provisioning. Established idempotent create/delete sequences to prevent orphaned files and directory clutter, resulting in improved storage hygiene and faster readiness for subsequent coursework deployments. This work lays groundwork for future feature expansions and enhances overall maintainability of the repository.
Activity
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Quality Metrics
Correctness94.0%
Maintainability94.0%
Architecture94.0%
Performance94.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookNonePythonUnknown
Technical Skills
AutomationBeautifulSoupCSV HandlingData AnalysisData CleaningData ExtractionData ProcessingData VisualizationDocument GenerationFeature EngineeringFile ConversionJupyter NotebookLinear RegressionMatplotlibOutlier Detection
Repositories Contributed To
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