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

Use this:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


# importing dataset

dataset=pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:,3:12].values
Y = dataset.iloc[:,13].values

#spliting dataset into test set and train set

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.20)

from sklearn.ensemble import RandomForestRegressor

regressor = RandomForestRegressor(n_estimators=20, random_state=0)  
regressor.fit(X_train, y_train) 

#feature importance

feature_importances = pd.DataFrame(regressor.feature_importances_,index = dataset.columns,columns=['importance']).sort_values('importance',ascending=False)

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'])

You converted your dataframe into a numpy array on this line:

x = x.values.reshape(-1,1)

The x.values part returns a numpy array, which doesn't have a columns attribute. That's why the code later throws an AttributeError when you try to access x.columns—it doesn't exist anymore.