Quant-of-Renmin-University/Quant_RUC
Oct 2025 – Oct 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
BeautifulSoupData AnalysisData CleaningData VisualizationLinear RegressionMatplotlib
Goodall2005
PROFILE
Goodall2005
Developed an end-to-end real estate analytics pipeline in the Quant_RUC repository, focusing on housing price and rent prediction. The solution integrated web scraping with BeautifulSoup and Selenium to automate data ingestion, followed by data cleaning, feature engineering, and sentiment analysis using Python and Pandas. Predictive modeling was implemented with Scikit-learn and results were exported to Excel for stakeholder reporting. The workflow was documented with a structured README to support onboarding and governance. This approach established a reproducible, refreshable data pipeline, enabling faster, data-driven pricing decisions and providing a foundation for ongoing model refinement and extended analytics capabilities.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
4,957
Activity Months1
Your Network
85 peopleWork History
October 2025
5 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for Quant_RUC: Delivered an end-to-end real estate analytics pipeline for housing price and rent prediction, including data ingestion via web scraping, cleaning, feature engineering, sentiment analysis, and predictive modeling. The implementation includes a Jupyter notebook and Python scripts that export results to Excel, and a documentation scaffold to support onboarding and governance. The work establishes a repeatable data workflow, enabling faster, data-driven pricing decisions and a foundation for ongoing model refinement. No major defects were reported this month; issues were addressed promptly as part of delivering the feature set.
5 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for Quant_RUC: Delivered an end-to-end real estate analytics pipeline for housing price and rent prediction, including data ingestion via web scraping, cleaning, feature engineering, sentiment analysis, and predictive modeling. The implementation includes a Jupyter notebook and Python scripts that export results to Excel, and a documentation scaffold to support onboarding and governance. The work establishes a repeatable data workflow, enabling faster, data-driven pricing decisions and a foundation for ongoing model refinement. No major defects were reported this month; issues were addressed promptly as part of delivering the feature set.
October 2025
Activity
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Quality Metrics
Correctness92.0%
Maintainability92.0%
Architecture92.0%
Performance88.0%
AI Usage28.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
BeautifulSoupData AnalysisData CleaningData VisualizationLinear RegressionMatplotlibPandasScikit-learnSeleniumWeb Scrapingdata preprocessingdata visualizationfeature engineeringmachine learningsentiment analysis
Repositories Contributed To
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