NON - PARAMETRIC MULTIPLE INPUTS PREDICTION MODEL FOR QUANTITY OF INFANT-FORMULA DEMANDED FOR CHILDREN AGED 0 - 24 MONTHS IN ILORIN-SOUTH, NIGERIA

Infant-formula is recommended by the World Health Organisation as safest form of complementary food to human breast milk. Breastfeeding duration and other imperative input-factors that govern quantity infant-formula demanded (a modulus variable) difficult model predict with high precision. However, infants based on their conventional rights have be given good nutrition. This study necessitated challenging process complex prediction complement milk, which may attributed differences in maternal characteristics at various geographical settlement areas. Owning nature differences, we propose a data-driven approach for handling larger-scale scenarios limited parametric modelling methods. aims investigating association infant -formula among breastfeeding mothers who children aged 0 – 24 months Ilorin-South Local Government Area, Nigeria. Primary data were collected from 744 mothers. We adopted appropriate non-parametric tests determine impact different independent groups covariate factors considered demanded. Multi-associations assessed negative binomial was formulated estimated durations. The results show increase leads In future intervention studies, significant effect demand might proper monitoring feeding.