Prediction of linear model on stunting prevalence with machine learning approach

An increase in the number of residents should be anticipated including health sector, especially problem stunting. Stunting children disrupts height and lack absorption nutrients. Information data drive change many areas such as health, entertainment, economics, business, other strategic areas. The stages carried out this study are initiating, developing linear models, making prediction results on machine learning models. testing with scikit-learn model a minimum variable 19 get best test results, namely polynomial regression pipeline mean absolute percentage error (MAPE) 0.02, root square (RMSE) 3.32, coefficient determination (R2) 1,00. Testing maximum 48 gets MAPE 0.00, RMSE 3.79 R2 1.00. an average 32 0.01, minimum, maximum, variables model.