📅 2025-07-15 — Session: Data Processing and Machine Learning Enhancements
🕒 03:10–07:00
🏷️ Labels: Census, Data Processing, Machine Learning, Public Engagement, Content Strategy
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance various data processing and machine learning tasks, focusing on census data reconciliation, machine learning pipeline setup, and content strategy for public engagement.
Key Activities
- Implemented a reconciliation layer for 2022 census data, including patch maps for department ID rewrites.
- Developed a linear growth correction method for population data from 2010 to 2025.
- Set up initial steps for random forest models in the EPH survey.
- Analyzed the ML pipeline structure and recommended improvements.
- Established a modular CI setup for machine learning pipelines.
- Reviewed and improved the EPH data pipeline.
- Handled schema changes in INDEC data processing.
- Constructed an alternative Consumer Price Index (CPI) for Argentina.
- Created a README structure for the CPI project.
- Resolved Pandas warnings and optimized GitHub Actions workflows.
- Developed a strategy for building a public presence as a computational economist.
Achievements
- Successfully implemented data reconciliation and patching methods.
- Improved machine learning pipeline setup and CI processes.
- Enhanced public engagement strategies through content planning.
Pending Tasks
- Further refinement of the CPI methodology and its documentation.
- Continued development of the dual authority content strategy.
Labels
census, data processing, machine learning, public engagement, content strategy