[USER (data scientist)]: Thanks! Now, how do I make predictions on the testing set? Please generate a NumPy array of predictions for the test set using the trained Logistic Regression model from the 'credit_customers' dataset.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
from sklearn.linear_model import LogisticRegression 
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(y_pred)

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: You can use the 'predict' method of the trained classifier to make predictions on the testing set. Here's the code for that:
'''
import pandas as pd 
from sklearn.linear_model import LogisticRegression 
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:
