Data imputation and comparison of custom ensemble models with existing libraries like XGBoost, Scikit learn, etc. for Predictive Equipment failure

This paper presents comparison of custom ensemble models with the models trained using existing libraries Like Xgboost, Scikit Learn, etc. in case of predictive equipment failure for the case of oil extracting equipment setup. The dataset that is used contains many missing values and the paper proposes different model-based data imputation strategies to impute the missing values. The architecture and the training and testing process of the custom ensemble models are explained in detail.