[USER (data scientist)]: Thanks! Now, how do I make predictions on the testing set? Please generate the code with the output in NumPy array type.
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, encoder_instance, encode_column, fetch_column, divide_dataset, create_LR_instance, classifier_training, classifier_predictions, calculate_conf_mat, calc_acc

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

# MY SOLUTION BEGIN:
