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
Developed district population analytics and forecasting capabilities for the BigData2025-Rev/p3 repository, focusing on enabling data-driven planning for 2030. Leveraged PySpark and Python to implement two core 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 encompassed data ingestion, transformation, modeling, and export of results as CSV files for downstream reporting. No major defects were reported, reflecting a focus on reproducibility and workflow robustness. This work established a foundation for district-level forecasting and early stagnation detection using machine learning.
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 peopleWork 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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