πŸ“… 2025-02-27 β€” Session: Representation Learning and BigQuery Integration for Socioeconomic Data

πŸ•’ 14:20–15:00
🏷️ Labels: Representation Learning, Bigquery, Socioeconomic Data, Autoencoder, Income Prediction
πŸ“‚ Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to explore the application of representation learning using autoencoders for improving income prediction from socioeconomic data, and to integrate census data into BigQuery for effective querying and cost management.

Key Activities

  • Discussed the significance of representation learning in uncovering hidden socioeconomic factors and improving income prediction using autoencoders.
  • Explored potential failure modes in semi-supervised learning with census data.
  • Outlined methods for making a census database queryable, including hosting options and uploading to BigQuery.
  • Provided a step-by-step guide for uploading census data to BigQuery, managing costs, and making it publicly queryable.
  • Evaluated BigQuery’s handling of table relationships and lack of foreign key support, suggesting strategies for optimization.
  • Described methods for querying random samples from BigQuery for machine learning pipelines.

Achievements

  • Developed a comprehensive understanding of how representation learning can be applied to socioeconomic data.
  • Established a workflow for integrating census data into BigQuery, ensuring efficient querying and cost management.

Pending Tasks

  • Implement the proposed experimental steps for validating the hypothesis on representation learning.
  • Further explore strategies for simulating foreign key behavior in BigQuery if needed.