[USER (data scientist)]: Thanks! How about predicting the customer satisfaction on the test set and evaluating the model? Please generate the code with the output in [string, matrix] type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd   
from sklearn.metrics import classification_report, confusion_matrix 
import pickle
from decision_company import read_csv_file, encoder_instance, encode_column, choose_data_types, get_columns, corr, create_figure, show_plots, set_plot_title, save_plot, remove_labels, divide_dataset, create_LR_instance, classifier_training, classifier_predictions, fetch_column, create_heatmap

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

# MY SOLUTION BEGIN:
