[USER (data scientist)]: Alright! What's the next step to split the data into training and testing sets? Please generate DataFrames and Series to represent the training and testing sets for both features and target variables from the 'credit_customers' dataset.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
from sklearn.model_selection import train_test_split 
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(X_train)

# save data
pickle.dump(X_train,open("./pred_result/X_train.pkl","wb"))
print(X_test)

# save data
pickle.dump(X_test,open("./pred_result/X_test.pkl","wb"))
print(y_train)

# save data
pickle.dump(y_train,open("./pred_result/y_train.pkl","wb"))
print(y_test)

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: To split the data, you can use the 'train_test_split' function from the 'sklearn.model_selection' module. Here's the code for that:
'''
import pandas as pd 
from sklearn.model_selection import train_test_split 
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:
