[USER (data scientist)]: Thanks! Can we use logistic regression to predict customer satisfaction based on the other variables? Please generate DataFrames for training and testing sets, and Series for training and testing labels from the 'credit_customers' dataset, train a logistic regression model on these sets.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
from sklearn.model_selection import train_test_split 
import pickle

# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE] 
</code1>
# YOUR SOLUTION END

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

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

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

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

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure thing! First, let's get the data ready for logistic regression by splitting it into features (X) and target (y). Then, we'll split the data into training and testing sets. Here's the code for that:
'''
import pandas as pd 
from sklearn.model_selection import train_test_split 
import pickle

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