We have already studied many techniques used for Missing Data Imputation . The majority of these techniques , that we studied, are or can be used in our final production-ready model. But when it comes to imputing something then there is always a chance of getting it better cause we are never sure if the values imputed are correct or not. Thus, to improve the imputation, we use Multiple imputations , i.e using more than one way to predict the values and then taking average or any other way to get the best suitable value. We have already seen a technique using similar logic, i.e. KNN Imputation , that uses the K-Nearest Neighbour Algorithm to find the best suitable value. These techniques are better known as " Multi-Variate Imputation ". Now, we would like to introduce you to a newer and better technique, which has now become a principal technique for Missing Data Imputation, known as MICE(Multi-variate Imputation of Chained Equation). Multi-variate Imputation of Chained Equa