Skip to content
forked from laporpe/STA_208

Machine Learning Modeling Approach to Analyze Standardized Agricultural Survey Data Collected in 21 Countries to determine factors influencing individual Market Orientation (MO) and Percent Poverty Index (PPI).

License

Notifications You must be signed in to change notification settings

cmdecesaris/RHoMIS

 
 

Repository files navigation

STA_208

Final Project at SML STA 208 at UC Davis Group Members:

  • Ana Boeriu
  • Christina De Cesaris
  • Mary-Francis LaPorte

The data for this project comes from "The Rural Household Multiple Indicator Survey (RHoMIS) data of 13,310 farm households in 21 countries" https://doi.org/10.7910/DVN/9M6EHS/6YYCTD by Mark van Wijk et al., 2019.

The write-up for this report is found in the report folder

Data used for the analyses are curated in the data directory. Finalized versions of scripts can be found in the folder finalized_code.

Misc folder contains code drafts.

About

Machine Learning Modeling Approach to Analyze Standardized Agricultural Survey Data Collected in 21 Countries to determine factors influencing individual Market Orientation (MO) and Percent Poverty Index (PPI).

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 77.7%
  • HTML 22.2%
  • Python 0.1%