[USER (data scientist)]:Great, now let's move on to the fifth question. Please generate a confusion matrix for the predictive model built using credit_history, employment, age, and credit_amount to estimate the likelihood of loan approval. Also, provide the accuracy of this model. Specifically, you can generate a logistic regression model to predict loan approval ('class') using credit history, employment, age, and credit amount features from the numerically encoded 'credit_customers' DataFrame.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
from sklearn.model_selection import train_test_split  
from sklearn.preprocessing import LabelEncoder  
from sklearn.linear_model import LogisticRegression  
from sklearn.metrics import confusion_matrix, accuracy_score
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("Confusion Matrix:\n", cm)  

# save data
pickle.dump(cm, open("./pred_result/Confusion_Matrix.pkl","wb"))
print("Accuracy:", acc) 

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you with that.
'''
import pandas as pd  
from sklearn.model_selection import train_test_split  
from sklearn.preprocessing import LabelEncoder  
from sklearn.linear_model import LogisticRegression  
from sklearn.metrics import confusion_matrix, accuracy_score
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:
