I am working with regression on the following dataset:
Which relates the crime rate per capita (last column) with 120 different metrics, such as education, wealth, police presence, etc in different neighbourhoods across the United States.
I have little to none experience with the mainstream approaches in problems like the one that appeared here. By looking at the dataset, one can verify a pattern-like missing of certain data in some of the rows, like the following: From what I've searched in internet, the main approach in a case of missing data (apparently it's even done by some languages like R) is to remove the rows where missing data appears.
However, if I try to that in this data-set, I end up with only 139 rows out of the 1994 that I have. I then tried to remove the columns, but then some very crucial information (all of the more 'problematic rows' refer to police presence in some areas).
I thus wanted to know what is the standard approach in this case. Clearly there is a pattern on the way the data is missing, but I am not sure on how to proceed to run around (or maybe even solve) this problem.