  Processed data are insightful, and crude data are obtuse. A serious threat to
data reliability is missing values. Such data leads to inaccurate analysis and
wrong predictions. We propose an efficient technique to impute the missing
value in the dataset based on correlation called FCMI (Feature Correlation
based Missing Data Imputation). We have considered the correlation of the
attributes of the dataset, and that is our central idea. Our proposed algorithm
picks the highly correlated attributes of the dataset and uses these attributes
to build a regression model whose parameters are optimized such that the
correlation of the dataset is maintained. Experiments conducted on both
classification and regression datasets show that the proposed imputation
technique outperforms existing imputation algorithms.
