[USER (data scientist)]: Yeah, let's use Label Encoding for 'savings_status' and 'employment', and One-Hot Encoding for 'credit_history'. Please generate the code with the output in dataframe type.
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, create_label_encoder, fit_transform_label_encoder, get_dummies, col_assign_val

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
<code1>
[GENERATE 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?

# MY SOLUTION BEGIN:
