Maskininlärning applicerat på data över biståndsinsatser : En studie i hur prediktiva modeller kan tillämpas för analys på Sida

Sammanfattning: The purpose of this master's thesis was to study if machine learning can be used asdecision support at the Swedish International Development Agency (Sida) in their work to provide financial aid. The aim was to examine the recurringphenomenon of increased number of aid disbursements towards the end of the year. A study and presentation of the data has been done to show the disbursementdistribution of Sida's operating departments. Moreover, qualitative interviews with different roles at Sida have been done to highlight the complexity of the agency and toexplain why and how different disbursement patterns occur. The approach has been to use classification models as well as regression models applied to data ofaid contributions from Sida's database. The classification models used were Decision Tree, k-Nearest Neighbour and Gradient Boosted Tree and thepurpose with the models was to illustrate which features of a contribution that are likely to be of importance for whether a disbursement occurs in December or earlier.The regression models used were linear models with the aim to predict if disbursements are likely to be delayed relative to the prognosis. The classificationmodel succeeded to point out three attributes that had influence on the classification result. The general conclusions of the report are that data ofcontributions generated in different IT-systems and various work routines at Sida's departments affect the quality of the data and the models’ accuracies negatively.Furthermore, insufficient amounts of data due to changes in Sida's information management has created difficulties when using data driven models to predict latedisbursements.