Define a Model for predicting missing values in Data Set I have the following problem: I'm searching for methods to predict randomly missing data in a given dataset. 
For example: I have a dataset which contains information about a product. This can be category, brand_name etc. Let's assume there are information missing of brand name for a specific product(4261 rows / 10 000). How can this information be predicted based on the information I have in my dataset?
You could try a classification approach like random forests for categorical data.
For example in your brand name assumption, Take your existing data and treat the brand name as the response variable. So when you give the values of the other features in the entry where the brand name is missing it should try to predict what the brand name is. 

The reason I am suggesting random forests is that it uses an ensemble approach. Please explore the working of Random Forests if you don't know the working behind it.