Missing Values Imputation in Food Consumption: An Analytical Study

Abstract

Missing values are an unavoidable trouble in some of actual world packages and the way to impute those missing values has end up a challenging problem in food consumption and production. Even though there are a few famous imputation techniques proposed, those techniques carry out poorly within side the estimation of food consumption with Missing Value. With this paper introduces an iterative imputation approach, KNN imputation method and median imputation method. These techniques are an example primarily based totally imputation method that takes benefit of the correlation of attributes. The achievable values for the missing values are expected from those nearest neighbor times. In addition, the iterative imputation permits all to be had values, consisting of the characteristic values within side the times with missing information and the imputed values from preceding new release to be applied for estimating the missing values. Specifically, the imputation approach can fill in all of the missing values with dependable records irrespective of the lacking charge of the food consumption dataset. We test our proposed approach on numerous food consumption datasets at extraordinary lacking costs in assessment with a few present imputation techniques. The experimental consequences recommend that the proposed approach receives a higher overall performance than different techniques in phrases of imputation accuracy and convergence speed.