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
During October 2025, this developer built an end-to-end real estate analytics pipeline in the Quant-of-Renmin-University/Quant_RUC repository, focusing on housing price and rent prediction. They engineered a workflow that ingests data through web scraping with BeautifulSoup and Selenium, then applies data cleaning, feature engineering, and sentiment analysis using Python and Pandas. The pipeline outputs results to Excel and is structured within a Jupyter Notebook, supporting reproducible analysis and regular data refreshes. Documentation scaffolding was established to aid onboarding and governance. The work delivered a robust, maintainable foundation for ongoing model refinement and streamlined data-driven decision-making in real estate analytics.
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
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