Implemented Data Reconciliation and Machine Learning Setup

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Implemented Data Reconciliation and Machine Learning Setup

  • Day: 2025-07-15
  • Time: 03:10 to 06:10
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Data Reconciliation, Machine Learning, Census Data, Python, Github Actions

Description

Session Goal

The session aimed to implement a data reconciliation layer for census data and set up machine learning models for the EPH survey.

Key Activities

  • Developed a reconciliation layer to align 2022 census data with older department IDs using Python and Pandas.
  • Implemented a linear growth correction methodology for population data from 2010 to 2025.
  • Executed a Python script for sampling census data via command line.
  • Set up initial configurations for Random Forest models related to the EPH survey.
  • Analyzed the machine learning pipeline structure and provided recommendations.
  • Established a modular CI setup for the machine learning pipeline using GitHub Actions.

Achievements

  • Successfully created a patch map and modular function for data preprocessing.
  • Developed a methodology for linear growth correction in population data.
  • Configured and executed data sampling scripts.
  • Initiated setup for machine learning models and CI integration.

Pending Tasks

  • Further refine the machine learning model setup and evaluate the pipeline’s performance.
  • Implement recommendations from the pipeline analysis for improved efficiency.

Evidence

  • source_file=2025-07-15.sessions.jsonl, line_number=0, event_count=0, session_id=c6049a9694746caefde8abffe4ff4c3fdc5cf52f583f51c3cf4215d25546744f
  • event_ids: []

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