[USER (data scientist)]: Thanks! How about predicting the customer satisfaction on the test set and evaluating the model? Please use the trained logistic regression model to predict on the test set, display the classification report (as a string) and confusion matrix (as a matrix) for the predictions.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
from sklearn.metrics import classification_report, confusion_matrix 
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(classification_report(y_test, y_pred),open("./pred_result/classification_report.pkl","wb"))

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: You got it! To do that, you can use the classification_report and confusion_matrix functions from the sklearn.metrics module. Here's the code for that:
'''
import pandas as pd 
from sklearn.metrics import classification_report, confusion_matrix 
import pickle

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