[USER (data scientist)]: Great, since there are no missing values, we can move on to the second question. I will generate the code to identify categorical variables in the credit_customers dataset and suggest encoding methods for converting them to numerical variables. Specifically, you can generate a dataframe and a pickle file to visualize and store the process of identifying nominal categorical variables in the 'credit_customers' dataset, followed by applying one-hot encoding to these variables.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
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("\nData after one-hot encoding:\n", credit_customers_encoded.head()) 

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you:
'''
import pandas as pd  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
