[USER (data scientist)]: Hey, I'm working on a project about properties in Flemington, Australia, and I could use some help. I want to find out how old the properties are on average and if there are any old buildings that could be renovated for sustainability. Can you help me with that? 
 
[YOU (AI assistant)]: Of course! I'd be happy to help. Do you have a dataset with the property information, like when each property was built? 
 
[USER (data scientist)]: Yeah, I've got a pandas DataFrame called "melbourne_housing" with a column named "YearBuilt" that has the year each property was built. 
 
[USER (data scientist)]: Let's start by looking at just the properties in Flemington.  Please generate a DataFrame to filter and display properties located in Flemington from the 'melbourne_housing' dataset, and then serialize this filtered DataFrame for later use.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import pickle

melbourne_housing = pd.read_csv("melb_data.csv") 

# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]
</code1>
# YOUR SOLUTION END:

print(flemington_housing)

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure thing! Here's the code to filter the dataset to only include properties in Flemington: 
