BigData2025-Rev/p3
Feb 2025 – Feb 2025
1 Month active
Languages Used
PythonSQL
Technical Skills
Data AnalysisData TransformationData Visualization (implied)Linear RegressionMachine LearningPySpark
rl4git
PROFILE
Rl4git
In February 2025, Cucucucu4github developed district population analytics and forecasting features for the BigData2025-Rev/p3 repository, focusing on enabling data-driven planning for 2030. Using PySpark, Python, and SQL, they implemented two scripts: one applied linear regression to historical data for population forecasting, while the other analyzed stagnation by calculating decade-over-decade percentage changes and flagging districts with minimal growth. The workflow included data ingestion, modeling, validation, and export to CSV for downstream reporting. The work established a reproducible analytics foundation, improved forecasting accuracy, and provided early stagnation detection to support resource allocation and policy decisions at the district level.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
144
Activity Months1
Your Network
16 people
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 — Delivered PySpark-based district population analytics and forecasting capabilities, enabling data-driven district planning for 2030. Implemented two Python scripts: one for forecasting district populations using linear regression on historical data, and another for stagnation analysis by computing decade-over-decade percentage changes and flagging districts with less than 2% change. Outputs are produced as CSV for downstream consumption and reporting. No major defects were reported this month; the focus was on building a reproducible analytics workflow. The work strengthens the data science foundation in BigData2025-Rev/p3 and provides actionable insights for resource allocation and policy decisions.
1 Commits • 1 Features
Feb 1, 2025February 2025 — Delivered PySpark-based district population analytics and forecasting capabilities, enabling data-driven district planning for 2030. Implemented two Python scripts: one for forecasting district populations using linear regression on historical data, and another for stagnation analysis by computing decade-over-decade percentage changes and flagging districts with less than 2% change. Outputs are produced as CSV for downstream consumption and reporting. No major defects were reported this month; the focus was on building a reproducible analytics workflow. The work strengthens the data science foundation in BigData2025-Rev/p3 and provides actionable insights for resource allocation and policy decisions.
February 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Data AnalysisData TransformationData Visualization (implied)Linear RegressionMachine LearningPySpark
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