'DataFrame' object has no attribute 'min_support

The error message "'DataFrame' object has no attribute 'min_support'" usually occurs when you try to use the `min_support` parameter on a Pandas DataFrame object that does not have this attribute.

`min_support` is an attribute of the `frequent_patterns` module of the `mlxtend` library, which is used for frequent itemset mining and association rule learning. To use the `min_support` parameter, you need to pass a Pandas DataFrame object with transaction data to the `apriori()` function of the `frequent_patterns` module.

Here's an example:

from mlxtend.frequent_patterns import apriori

# create a Pandas DataFrame with transaction data
transactions = [['milk', 'bread', 'eggs'], ['bread', 'butter'], ['milk', 'bread', 'butter'], ['milk', 'eggs']]

df = pd.DataFrame(transactions)

# apply the apriori algorithm with a minimum support of 0.5
frequent_itemsets = apriori(df, min_support=0.5, use_colnames=True)

print(frequent_itemsets)

In this example, `apriori()` is applied to a Pandas DataFrame object `df` with transaction data, and the `min_support` parameter is set to 0.5. The resulting frequent itemsets are printed to the console.