-1 down vote favorite I have a dataset including missing data for most of the variables. Assume the dataset is as follows:
Obs. var1 var2 var3 var4 var5 var6 1 x11 x12 x13 x14 Nan Nan 2 x21 x22 x23 Nan x25 x26 3 x31 x32 x33 x34 x35 x36 ... n xn1 xn2 xn3 xn4 Nan xn6
I have split the dataset to d1 where we have complete data for all variables and d2 where all records have at least one missing variable.
I made different models using KNN: To predict the values of var5 and var6 for the first observation, I used d1 (dataset without missing value) and modeled on var1, var2, var3 and var4.
To predict the value of var5 for the last observation, I used d1 and modeled on var1, var2, var3, var4, and var5.
Does my approach make sense?! Any suggestion are welcome. Thank you.