title: How to handle missing values in the dataset
body: <p>I have a simple classification problem, which I am trying to address through neural network using keras. There is numeric dataset, of size 26000 * 17.But the problem is that, there are a lot of missing values (null values) in the dataset. Data is quite sensitive, so neither I can ignore all rows containing null values nor replace the null values in the data with average, mean or any standard number. There is also constraint of not using KNN imputation to replace missing entries.
  What is the best way to handle such dataset? </p>