Check the Pandas documentation, but I think

X_train = df_train.drop(['ID','TARGET'], axis=1).values

.values returns a numpy array, not a Pandas dataframe. An array does not have a columns attribute.

remove_features_identical - if you pass this an array, make sure you are only using array, not dataframe, features. Otherwise, make sure you pass it a dataframe. And don't use variable names like DataFrame.

The problem is that train_test_split(X, y, ...) returns numpy arrays and not pandas dataframes. Numpy arrays have no attribute named columns

If you want to see what features SelectFromModel kept, you need to substitute X_train (which is a numpy.array) with X which is a pandas.DataFrame.

selected_feat= X.columns[(sel.get_support())]

This will return a list of the columns kept by the feature selector.

If you wanted to see how many features were kept you can just run this:

sel.get_support().sum()  # by default this will count 'True' as 1 and 'False' as 0

Using .values on a pandas dataframe gives you a numpy array. This will not contain column names and such. You do this when setting X like this:

X = dataset[['Read?', 'x1', .. ,'x47']].values 

But then you try to get the column names from X (which it does not have) by writing X.columns here:

coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])

So store your column names in a variable or input them again, like this:

coeff_df = pd.DataFrame(regressor.coef_, ['Read?', 'x1', .. ,'x47'], columns=['Coefficient'])

The solution:

The given dataset was already an array, so I didn’t need to call .values.

You are using the right method but in a wrong way :)

nan_to_num is a method of numpy module, not numpy.ndarray. So instead of calling nan_to_num on you data, call it on numpy module giving your data as a paramter:

import numpy as np
data = np.array([1,2,3,np.nan,np.nan,5])
data_without_nan = np.nan_to_num(data)

prints:

array([1., 2., 3., 0., 0., 5.])

In your example:

import numpy as np
val = np.nan_to_num(setTo.ravel())

I couldn't find the minute in the tutorial, but may be it's just a consequence of copy-paste.

In the function scale_dataset you make data a numpy array and then you assign that value to train variable. But when you come again with scale_dataset for valid data set you want to use this `train' data set as a pandas dataframe but in that moment it's a numpy array.

My common sense tells me you want to use valid data set instead of train and so on like this:

train, X_train, y_train = scale_dataset(train, oversample = True)
valid, X_train, y_train = scale_dataset(valid, oversample = False)
test, X_train, y_train = scale_dataset(test, oversample = False)