Neural Network Challenge-: House price prediction

#21

So i have to apply this one by one to each column?

#22

just make a for loop to do this.
for col in columns :
df[ col ] = encoder.fit_transform(df[ col ])

It will do your task.

#23

Can you please code it?

#24

which code snippet do you need ?

#25

Just i want to convert categorical to numerical data. After that i can do all the work

#26

You are giving the Code?

#27

use this.
it will work

#28

That previous one you told me?

#29

yeah. Use that one and let me know if there is any error

#30

argument must be a string or a number

#31

Screenshot (81)
Screenshot (81)1366×768 57.1 KB

#32

Error is argument must be a string or a number

#33

Are you there???

#34

sorry to respond so late.
Do this data contains any NaN values ?

#35

Yes this data also contains nan values

#36

The NAN values are in many columns

#37

so , before converting or encoding them you need to fill those NaN values.
For now ,
In categorical Columns you can fill them with a new class.
In Numerical Columns , you can fill them with mean of the values of that column.

#38

How will i do in categorical columns?

#39

Select those columns as
df[ columns ] = df[ columns ].fillna(“unknown”)

#40

how can i select those columns having nan values