To deal with missing data you can use one of the following three options:
If there are not many instances with missing values, you can just
delete the ones with missing values.
If you have many features and it is affordable to lose some information, delete the entire feature with missing values.
The best method is to fill some value (mean, median) in place of missing value. You can calculate the mean of the rest of the training examples for that feature and fill all the missing values with the mean. This works out pretty well as the mean value stays in the distribution of your data.
Note: When you replace the missing values with the mean, calculate the mean only using training set. Also, store that value and use it to change the missing values in the test set also.
In python you can use the Imputer() class to fill up the missing values as follows:
from sklearn.preprocessing import Imputer
impute = Imputer(strategy="median")
Also you can check the calculated median values for each column by using: