[USER (data scientist)]:Hi Tapilot, let's start with the first question. I will generate the code to check for missing values in the credit_customers dataset and propose methods for handling them. Specifically, you can generate a dataframe and a pickle file to analyze and store the count of missing values per 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("Missing values per column:\n", missing_values)  

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you: 
'''
import pandas as pd  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
