[USER (data scientist)]: Hey, I've got this dataset of credit customers, and I'm trying to see how loan approval rates are related to factors like credit history, employment status, age, and credit amount. Can you help me out? 

[YOU (AI assistant)]: Absolutely! Let's start by loading your dataset. Is it in a CSV file? 

[USER (data scientist)]: Yeah, it's called "credit_customers.csv". 

[YOU (AI assistant)]: Cool! Let's load it up using pandas: 
'''
import pandas as pd 

# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Nice one! Now, I need to turn the 'class' column into binary values, like 1 for 'good' and 0 for 'bad'. Please create and display a Series of a binary representation of the 'class' column in the 'credit_customers' dataset.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
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['class_binary'])

# save data
pickle.dump(credit_customers['class_binary'],open("./pred_result/credit_customers_class_binary.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: No problem! Let's convert the 'class' column using a lambda function:
'''
import pandas as pd 
import pickle
 
# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
