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I read up some books on missing values. They have mentioned that listwise deletion is the least preferred method even though the sample size maybe be large (Newman, D. A. 2014. Missing Data: Five Practical Guide-lines.Organizational Research Methods 17: 372–411)

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Next, I went to Kaggle to checkout how the missing value is handled for this data set https://www.kaggle.com/c/house-prices-advanced-regression-techniques. However the top rated code handle it by totally removing it. Some other top code use mean, mode etc. but so far I have not found any use Multiple Imputation, Maximum Likelihood etc. Moreover, the missingness (MCAR / MAR) was not determined.

Hence, I am confused in which situation do we need to go into details to handle missing value. What is the industry standard approach?

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