Pemangkasan lanjutan hasil Apriori

Analisis Market Basket dengan Python

Isaiah Hull

Visiting Associate Professor of Finance, BI Norwegian Business School

Aplikasi

Promosi Silang

Gambar ini menunjukkan contoh promosi silang.

Agregasi

Gambar ini menunjukkan contoh agregasi.

Algoritma Apriori

Daftar Bersarang

Gambar ini menunjukkan contoh daftar bersarang item dalam transaksi.

One-Hot Encoding

Gambar ini menunjukkan one-hot encoding untuk transaksi.

Algoritma Apriori

Gambar ini mengilustrasikan penerapan algoritma Apriori pada himpunan 4 item generik.

Algoritma Apriori

import pandas as pd
import numpy as np
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori

itemsets = np.load('itemsets.npy')
print(itemsets)

[['EASTER CRAFT 4 CHICKS'],
['CERAMIC CAKE DESIGN SPOTTED MUG', 'CHARLOTTE BAG APPLES DESIGN'],
['SET 12 COLOUR PENCILS DOLLY GIRL'],
...
['JUMBO BAG RED RETROSPOT', ... 'LIPSTICK PEN FUSCHIA']]

Algoritma Apriori

# One-hot encode data
encoder = TransactionEncoder()
onehot = encoder.fit(itemsets).transform(itemsets)
onehot = pd.DataFrame(onehot, columns = encoder.columns_)

# Apply Apriori algorithm and print
frequent_itemsets = apriori(onehot, use_colnames=True, min_support=0.001)
print(frequent_itemsets)

      support                                           itemsets
0    0.001504                               ( DOLLY GIRL BEAKER)
1    0.002256                         ( RED SPOT GIFT BAG LARGE)
...
428  0.001504  (BIRTHDAY CARD, RETRO SPOT, JUMBO BAG RED RETR...

Hasil algoritma Apriori

print(len(data.columns))

print(len(frequent_itemsets))

rules = association_rules(frequent_itemsets)

Aturan asosiasi

print(rules['consequents'])

0                   (DOTCOM POSTAGE)
                        ... 
9                 (HERB MARKER THYME)
                        ...
234        (JUMBO BAG RED RETROSPOT)
235         (WOODLAND CHARLOTTE BAG)
236    (RED RETROSPOT CHARLOTTE BAG)
237       (STRAWBERRY CHARLOTTE BAG)
238      (CHARLOTTE BAG SUKI DESIGN)
Name: consequents, Length: 239, dtype: object

Penyaringan dengan banyak metrik

targeted_rules = rules[rules['consequents'] == {'HERB MARKER THYME'}].copy()

filtered_rules = targeted_rules[(targeted_rules['antecedent support'] > 0.01) &
                        (targeted_rules['support'] > 0.009) &
                        (targeted_rules['confidence'] > 0.85) &
                        (targeted_rules['lift'] > 1.00)]

print(filtered_rules['antecedents'])

9        (HERB MARKER BASIL)
25     (HERB MARKER PARSLEY)
27    (HERB MARKER ROSEMARY)
Name: antecedents, dtype: object

Pengelompokan produk

Gambar menunjukkan denah toko: kotak dipasangkan dengan tas, dan papan tanda dengan lilin.

Gambar menunjukkan denah toko: kotak dipasangkan dengan lilin, dan papan tanda dengan tas.

Gambar menunjukkan denah toko: kotak dipasangkan dengan papan tanda, dan lilin dengan tas.

Agregasi dan disosiasi

# Load aggregated data
aggregated = pd.read_csv('datasets/online_retail_aggregated.csv')

# Compute frequent itemsets
onehot = encoder.fit(aggregated).transform(aggregated)
data = pd.DataFrame(onehot, columns = encoder.columns_)
frequent_itemsets = apriori(data, use_colnames=True)

# Compute standard metrics
rules = association_rules(frequent_itemsets)

# Compute Zhang's rule
rules['zhang'] = zhangs_rule(rules)

Aturan Zhang

# Print rules that indicate dissociation
print(rules[rules['zhang'] < 0][['antecedents','consequents']])

  antecedents consequents
2       (bag)    (candle)
3    (candle)       (bag)
4      (sign)       (bag)
5       (bag)      (sign)

Memilih denah toko

Gambar menunjukkan denah toko: kotak dipasangkan dengan tas, dan papan tanda dengan lilin.

Ayo berlatih!

Analisis Market Basket dengan Python