I hope this message finds you well. I have been trying to impute missing values in my dataset using datawig library. However when I use datawig library to impute the missing values in my dataset. It imputes each and every other column while leaving behind two columns. Both of the columns are of dtype: object. However, it imputes other object columns. I had tried your recommendation by increasing the precision_threshold = 0.80 which also did not do any good. Any recommendation of making it better. Here is the code along with the visualization of my dataset:

[SAMNaqvi1212]

I hope this message finds you well. I have been trying to impute missing values in my dataset using datawig library. However when I use datawig library to impute the missing values in my dataset. It imputes each and every other column while leaving behind two columns. Both of the columns are of dtype: object. However, it imputes other object columns. I had tried your recommendation by increasing the precision_threshold = 0.80 which also did not do any good. Any recommendation of making it better. Here is the code along with the visualization of my dataset: df.tail(155).

The code to impute the missing values is as follows: import datawig df = datawig.SimpleImputer.complete(df, precision_threshold=0.80)

df.isnull().sum()
PassengerId       0
HomePlanet        0
CryoSleep         0
Cabin           199
Destination       0
Age               0
VIP               0
RoomService       0
FoodCourt         0
ShoppingMall      0
Spa               0
VRDeck            0
Name            200
Transported       0
dtype: int64

The missing values for the column named Cabin and Name were left and were not imputed for I do not know what reason. Also before applying datawig imputation the number of missing values in Name and Cabin column were the same. Any kind help would be appreciated Thanks!!!!

Originally posted by @SAMNaqvi1212 in https://github.com/awslabs/datawig/issues/144#issuecomment-1169330652