[USER (data scientist)]: We want to find the count of clients in the final filtered dataset who reside in rented or owned housing, excluding those living rent-free. Please provide the result type as a count of clients residing in rented or owned housing. 

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

# Filter the clients residing in rented or owned housing
rented_owned_housing = high_credit_long_duration[(high_credit_long_duration['housing'] == 'rent') | (high_credit_long_duration['housing'] == 'own')]

# Calculate the count of clients residing in rented or owned housing
result_count = rented_owned_housing.shape[0]
print(result_count)

# save data
pickle.dump(open("./pred_result/result_count_3.pkl","wb"))
'''

[USER (data scientist)]: Shouldn't final dataset be `stable_employment`?
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(result_count)

# save data
pickle.dump(result_count,open("./pred_result/result_count_3.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure.
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")

# YOUR SOLUTION BEGIN:
