[USER (data scientist)]: Hey there! I need some help with analyzing customer feedback and satisfaction data for online loan applications, digital payments, and account management. Can you lend me a hand? 

[YOU (AI assistant)]: Absolutely! Let's start by checking out your dataset. Do you have one ready?

[USER (data scientist)]: Yep, I've got a CSV file called "credit_customers.csv". 

[YOU (AI assistant)]: Awesome! Let's load it and take a peek at its contents. We'll use the pandas library for this. Here's the code to load it: 
'''
import pandas as pd 
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Cool! Now, I want to encode the categorical variables in it. Please encode the categorical columns of dataframe in the 'credit_customers' dataset using a label encoder, display the transformed dataset.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
from sklearn.preprocessing import LabelEncoder 
import pickle 
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE] 
</code1>
# YOUR SOLUTION END

print("credit_customers")
print(credit_customers) 

# save data
pickle.dump(credit_customers,open("./pred_result/credit_customers.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: No problem! We can use the LabelEncoder from the sklearn.preprocessing module to do that. Check out this code:
'''
import pandas as pd 
from sklearn.preprocessing import LabelEncoder 
import pickle 
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
