Learning Data Mining with Python

Recommending Movies Using Affinity Analysis
print("Rule: If a person recommends {0} they will also
recommend {1}".format(premise, conclusion))
print(" - Confidence:
{0:.3f}".format(rule_confidence[(premise, conclusion)]))
print("")
The result is as follows:
Rule #1
Rule: If a person recommends frozenset({64, 56, 98, 50, 7}) they will
also recommend 174
- Confidence: 1.000
Rule #2
Rule: If a person recommends frozenset({98, 100, 172, 79, 50, 56})
they will also recommend 7
- Confidence: 1.000
Rule #3
Rule: If a person recommends frozenset({98, 172, 181, 174, 7}) they
will also recommend 50
- Confidence: 1.000
Rule #4
Rule: If a person recommends frozenset({64, 98, 100, 7, 172, 50}) they
will also recommend 174
- Confidence: 1.000
Rule #5
Rule: If a person recommends frozenset({64, 1, 7, 172, 79, 50}) they
will also recommend 181
- Confidence: 1.000
The resulting printout shows only the movie IDs, which isn't very helpful without
the names of the movies also. The dataset came with a file called u.items, which
stores the movie names and their corresponding MovieID (as well as other
information, such as the genre).
We can load the titles from this file using pandas. Additional information about
the file and categories is available in the README that came with the dataset.
The data in the files is in CSV format, but with data separated by the | symbol;
it has no header and the encoding is important to set. The column names were
found in the README file.
movie_name_filename = os.path.join(data_folder, "u.item")
movie_name_data = pd.read_csv(movie_name_filename, delimiter="|",
header=None, encoding = "mac-roman")
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