households

There are 9 repositories under households topic.

  • wiebket/delarchetypes

    A pipeline to construct residential electricity consumer archetypes from the South African Domestic Electrical Load (DEL) database.

    Language:Python3220
  • wiebket/delretrieve

    This package facilitates data retrieval from a SQL Server installation of the South African Domestic Electrical Load (DEL) database.

    Language:Python2140
  • Ashish-3/House-prices-in-France

    The goal of this project is to answer the following question: Where is a “good place” to buy a house in France and at what price? see readme file for info.

    Language:HTML1111
  • wiebket/delprocess

    Extract, clean, resample and enumerate load profile and survey data from a local file hierarchy retrieved from the South African Domestic Electrical Load (DEL) database.

    Language:Python11100
  • DIY4E1/MathDIY

    Democracy (D) and Internet (I) are Yours. MathDIY is a simple mathematical notation for describing business and political decision making, capturing its motivation, tensions and context.

    Language:HTML0100
  • if-else-return-null/ledgersmart

    An open source money management app for individuals, households, organizations and business

    Language:JavaScript0200
  • dataknut/HEEP2

    Repo supporting work with BRANZ on the HEEP2 study

    Language:HTML10
  • megamillions/Random-Chore-Assignment-Emailer

    Creates a log of family members to send chores. Randomly assigns chores to family members, and emails each their assigned chore. Does not assign the same chore twice. Remember to schedule to run automatically once a week or as desired. Inspired by Al Sweigart's Automate the Boring Stuff with Python: Chapter 16.

    Language:Python20
  • she-osprey/IPWK10-CORE-ZINDI-COVID-HACKATHON

    ZINDI - HACKATHON: The task is to predict the percentage of households that fall into a particularly vulnerable bracket - large households who must leave their homes to fetch water - using 2011 South African census data. Solving this challenge will show that with machine learning it is possible to use easy-to-measure stats to identify areas most at risk even in years when census data is not collected.

    Language:Jupyter Notebook10