[USER (data scientist)]: Yep, I'd like to encode 'savings_status' and 'employment' using LabelEncoder, and create dummy variables for 'credit_history'. Please generate a DataFrame of the processed data suitable for clustering, with label encoding and one-hot encoding applied.
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("data_for_clustering:\n", data_for_clustering) 

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: No problem! Let's encode those categorical variables. And should we normalize the data before clustering?
'''
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:
