📅 2025-07-15 — Session: Implemented Data Reconciliation and Machine Learning Setup
🕒 03:10–06:10
🏷️ Labels: Data Reconciliation, Machine Learning, Census Data, Python, Github Actions
📂 Project: Dev
⭐ Priority: MEDIUM
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.