[USER (data scientist)]: Hey there! I'm working on a project to predict loan approvals, and I could use some help. Can you assist me?

[YOU (AI assistant)]: Of course! I'd be happy to help you with your loan approval prediction project. We can use a logistic regression model for this task. Do you have a dataset that we can work with?

[USER (data scientist)]: Yeah, I've got a CSV file called "credit_customers.csv". 

[YOU (AI assistant)]: Awesome! Let's start by loading that dataset using pandas. Here's how you can do it: 
'''
import pandas as pd 

# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Cool, thanks! Now, I need to convert some categorical features to numbers. How do I do that? Please generate a DataFrame by encoding the 'credit_history', 'employment', and 'class' columns in the 'credit_customers' dataset using a label encoder.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
from sklearn.preprocessing import LabelEncoder
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE] 
</code1>
# YOUR SOLUTION END

print(credit_customers)

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: No problem! You can use the 'LabelEncoder' from the 'sklearn.preprocessing' module to convert categorical features to numerical values. Here's how:
'''
import pandas as pd 
from sklearn.preprocessing import LabelEncoder
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
