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# Picaps
- URL: https://gnucoopcom-u2056.vm.elestio.app/projects/picaps/
- Published: 2024-06-03T09:00:00.000Z
- Updated: 2024-06-03T09:00:00.000Z
- Description: A study of data from families involved in the PICAPS project was conducted. The main objective of the survey was to understand the characteristics of school-age children who do not attend school. The information collected concerned both personal data - composition of the househol
- Author: Gnucoop Soc. Coop.
- Tags: #project, #tk-p1181, Data analysis, #Import 2026-09-12 16:33

__Project facts__
| Partner      | [CIAI ONLUS](https://www.ciai.it/?ref=gnucoopcom-u2056.vm.elestio.app)                     |
| ------------ | ------------------------------------------------------------------------------------------ |
| Location     | Burkina Faso                                                                               |
| Technology   | Jupiter + Python                                                                           |
| Donor        | AICS                                                                                       |
| Duration     | 3 months                                                                                   |
| Status       | Completed                                                                                  |
| Project link | [Github Repository](https://github.com/gnucoop/picaps?ref=gnucoopcom-u2056.vm.elestio.app) |

### The project

A study of data from families involved in the PICAPS project was conducted. The main objective of the survey was to understand the characteristics of school-age children who do not attend school. The information collected concerned both personal data - composition of the household, age, sex - and socio-economic data - schooling of family members, income, work activities. A first part of the analysis was purely descriptive, with the aim of defining more precisely the characteristics of out-of-school children. The second part of the analysis sought to identify which characteristics of the household were most influential in the dropout phenomenon, in order to construct a general model. For this purpose, a classification algorithm was trained to provide probability rates of dropout based on household characteristics. The final result of this analysis is a set of classification rules that can be used to prevent dropout through the planning of more targeted interventions of socio-educational support.

Further information at [with the full technical report](https://docs.google.com/document/d/1vxqVAI9eLchCc082feH-ziC1NXyqRgQ-bM4wMFTDs9Y/edit?usp=sharing&ref=gnucoopcom-u2056.vm.elestio.app)