DescriptionVariable selection is a significant pre-processing task for prediction in the field of data mining and machine learning, and involves the selection of a subset of relevant variables. Almost all researchers have faced the problem of missing data, which can occur due to nonresponse or loss of information. This thesis develops a new variable selection technique for dealing with missing data. Relief is an algorithm for estimating the quality of each variable and is applicable to categorical or continuous data. This thesis presents a new variable selection method, RM-Relief, by extending Relief to select the variables in a regression with missing data. RM-Relief weights all predictor variables by assigning bins for a response variable and estimating the conditional probability of unknown instances. Results on artificial and real-world datasets indicate that RM-Relief works well on regression problems with missing data.