[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 
from decision_company import read_csv_file

# Load the dataset  
credit_customers = read_csv_file("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 generate the code with the output in series type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd   
import pickle
from decision_company import read_csv_file, create_subplots, df_copy, cast_to_a_dtype, categoricalIndex, categorical_codes, scatter_fig_instance, set_plot_split_title, set_layout, show_plots, save_plot, fetch_column

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

# MY SOLUTION BEGIN:
