[USER (data scientist)]: Yeah, let's use Label Encoding for 'savings_status' and 'employment', and One-Hot Encoding for 'credit_history'. Please generate a DataFrame that applies Label Encoding to 'savings_status' and 'employment', and One-Hot Encoding to 'credit_history'.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
from sklearn.preprocessing import LabelEncoder 
import pickle
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first

# Load the dataset 
credit_customers = read_csv_file("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)]: Sounds good, let's apply the encodings. And do you want me to normalize the data? If so, which method should I use?
'''
import pandas as pd 
from sklearn.preprocessing import LabelEncoder 
import pickle
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first

# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
