Rekap transaksi

Analisis Keranjang Belanja di R

Christopher Bruffaerts

Statistician

Poin penting dalam analisis keranjang belanja

Analisis keranjang belanja

Fokus pada apa, bukan berapa banyak;
yaitu apa saja yang ada di keranjang pelanggan?

satu_keranjang_belanja

Metrik utama

Support
Confidence
Lift

Catatan kehati-hatian

Kumpulan aturan yang diekstrak bisa sangat besar.
Jangan meninjau atau menampilkan semua aturan; selalu gunakan subset atau fungsi head/tail.

Dataset Groceries

Kembali ke Toko Kelontong

Groceries

Dataset dari paket arules

# Loading the arules package
library(arules)

# Loading the Groceries dataset
data(Groceries)

summary(Groceries)

Ringkasan Groceries

transactions as itemMatrix in sparse format with
 9835 rows (elements/itemsets/transactions) and
 169 columns (items) and a density of 0.02609146 

most frequent items:
      whole milk other vegetables       rolls/buns             soda           yogurt 
            2513             1903             1809             1715             1372 
         (Other) 
           34055 

element (itemset/transaction) length distribution:
sizes
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17 
2159 1643 1299 1005  855  645  545  438  350  246  182  117   78   77   55   46   29 
  18   19   20   21   22   23   24   26   27   28   29   32 
  14   14    9   11    4    6    1    1    1    1    3    1 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   4.409   6.000  32.000 

includes extended item information - examples:
       labels  level2           level1
1 frankfurter sausage meat and sausage
2     sausage sausage meat and sausage
3  liver loaf sausage meat and sausage

Kepadatan Groceries

# Plotting a sample of 200 transactions
image(sample(Groceries, 200))

citra_groceries_200

¹ Kepadatan matriks item adalah 2,6%.

Item terpopuler dan terendah

Item terpopuler

itemFrequencyPlot(Groceries,type="relative",
                  topN=10,horiz=TRUE,col='steelblue3')

plot frekuensi item_groceries

Item paling tidak populer

par(mar=c(2,10,2,2), mfrow=c(1,1))
barplot(sort(table(unlist(LIST(Groceries))))[1:10],
        horiz = TRUE,las = 1,col='orange')

plot frekuensi item_groceries_terendah

Tabel silang menurut indeks

Tabel kontingensi

# Contingency table
tbl = crossTable(Groceries)
tbl[1:4,1:4]

            frankfurter sausage liver loaf ham
frankfurter         580      99          7  25
sausage              99     924         10  49
liver loaf            7      10         50   3
ham                  25      49          3 256

Tabel kontingensi terurut

# Sorted contingency table
tbl = crossTable(Groceries, sort = TRUE)
tbl[1:4,1:4]

                whole milk other vegetables rolls/buns soda
whole milk             2513              736        557  394
other vegetables        736             1903        419  322
rolls/buns              557              419       1809  377
soda                    394              322        377 1715

Tabel silang menurut nama item

Tabel kontingensi

# Counts
tbl['whole milk','flour']

[1] 83

# Uji chi-kuadrat
crossTable(Groceries, measure='chi')['whole milk', 'flour']

[1] 0.003595389

Tabel kontingensi dengan metrik lain

crossTable(Groceries, measure='lift',sort=T)[1:4,1:4]

                 whole milk other vegetables rolls/buns      soda   
whole milk               NA        1.5136341   1.205032 1.571735
other vegetables  1.5136341               NA   1.197047 0.9703476 
rolls/buns        1.2050318        1.1970465         NA 1.1951242 
soda              0.8991124        0.9703476   1.195124        NA

Dataset MovieLens

MovieLens: Sistem rekomendasi berbasis web yang menyarankan film untuk ditonton pengguna.

movielens

Mari menonton film!

Analisis Keranjang Belanja di R