In [1]:
import pandas as pd
from mlxtend.frequent_patterns import apriori, association_rules
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
df = pd.read_excel('Online Retail.xlsx')
df.head()
Out[2]:
InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice CustomerID Country
0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 2010-12-01 08:26:00 2.55 17850.0 United Kingdom
1 536365 71053 WHITE METAL LANTERN 6 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 2010-12-01 08:26:00 2.75 17850.0 United Kingdom
3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 2010-12-01 08:26:00 3.39 17850.0 United Kingdom

DATA preparation

In [3]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 541909 entries, 0 to 541908
Data columns (total 8 columns):
 #   Column       Non-Null Count   Dtype         
---  ------       --------------   -----         
 0   InvoiceNo    541909 non-null  object        
 1   StockCode    541909 non-null  object        
 2   Description  540455 non-null  object        
 3   Quantity     541909 non-null  int64         
 4   InvoiceDate  541909 non-null  datetime64[ns]
 5   UnitPrice    541909 non-null  float64       
 6   CustomerID   406829 non-null  float64       
 7   Country      541909 non-null  object        
dtypes: datetime64[ns](1), float64(2), int64(1), object(4)
memory usage: 33.1+ MB
In [4]:
df['Description'] = df['Description'].str.strip()
df = df[df['Description']!= 'POSTAGE']
 
# Dropping the rows without any invoice number
df.dropna(axis = 0, subset =['InvoiceNo'], inplace = True)
df['InvoiceNo'] = df['InvoiceNo'].astype('str')
 
# Dropping all transactions which were done on credit
df = df[~df['InvoiceNo'].str.contains('C')]

df = df[df['Description'].str.startswith('?')!=True]
df.shape
Out[4]:
(531428, 8)
In [5]:
df.describe()
Out[5]:
Quantity UnitPrice CustomerID
count 531428.000000 531428.000000 396825.000000
mean 10.275836 3.790413 15301.354595
std 159.551926 40.242194 1709.881541
min -9600.000000 -11062.060000 12346.000000
25% 1.000000 1.250000 13975.000000
50% 3.000000 2.080000 15159.000000
75% 10.000000 4.130000 16801.000000
max 80995.000000 13541.330000 18287.000000
In [6]:
sns.heatmap(df.corr(),annot = True)
Out[6]:
<AxesSubplot:>
In [7]:
def missing_values(x):
    print(x.isna().sum())
    print("*"*50)
    print('Percentage of missing values is')
    print(x.isnull().sum()*100/len(x))

    
missing_values(df)
    

InvoiceNo           0
StockCode           0
Description      1455
Quantity            0
InvoiceDate         0
UnitPrice           0
CustomerID     134603
Country             0
dtype: int64
**************************************************
Percentage of missing values is
InvoiceNo       0.000000
StockCode       0.000000
Description     0.273791
Quantity        0.000000
InvoiceDate     0.000000
UnitPrice       0.000000
CustomerID     25.328549
Country         0.000000
dtype: float64
In [8]:
df = df.drop(columns = 'CustomerID')
df.head()
Out[8]:
InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice Country
0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 2010-12-01 08:26:00 2.55 United Kingdom
1 536365 71053 WHITE METAL LANTERN 6 2010-12-01 08:26:00 3.39 United Kingdom
2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 2010-12-01 08:26:00 2.75 United Kingdom
3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 2010-12-01 08:26:00 3.39 United Kingdom
4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 2010-12-01 08:26:00 3.39 United Kingdom
In [9]:
df = df.dropna()
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 529973 entries, 0 to 541908
Data columns (total 7 columns):
 #   Column       Non-Null Count   Dtype         
---  ------       --------------   -----         
 0   InvoiceNo    529973 non-null  object        
 1   StockCode    529973 non-null  object        
 2   Description  529973 non-null  object        
 3   Quantity     529973 non-null  int64         
 4   InvoiceDate  529973 non-null  datetime64[ns]
 5   UnitPrice    529973 non-null  float64       
 6   Country      529973 non-null  object        
dtypes: datetime64[ns](1), float64(1), int64(1), object(4)
memory usage: 32.3+ MB
In [10]:
data = df[df['Quantity']>=0]
data.info()
data.head()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 529561 entries, 0 to 541908
Data columns (total 7 columns):
 #   Column       Non-Null Count   Dtype         
---  ------       --------------   -----         
 0   InvoiceNo    529561 non-null  object        
 1   StockCode    529561 non-null  object        
 2   Description  529561 non-null  object        
 3   Quantity     529561 non-null  int64         
 4   InvoiceDate  529561 non-null  datetime64[ns]
 5   UnitPrice    529561 non-null  float64       
 6   Country      529561 non-null  object        
dtypes: datetime64[ns](1), float64(1), int64(1), object(4)
memory usage: 32.3+ MB
Out[10]:
InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice Country
0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 2010-12-01 08:26:00 2.55 United Kingdom
1 536365 71053 WHITE METAL LANTERN 6 2010-12-01 08:26:00 3.39 United Kingdom
2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 2010-12-01 08:26:00 2.75 United Kingdom
3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 2010-12-01 08:26:00 3.39 United Kingdom
4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 2010-12-01 08:26:00 3.39 United Kingdom

Next step is to check whether the data has duplicate values

In [11]:
def duplicate(x):
    print(x.duplicated().sum())
    

duplicate(data)

5231

We have found that there are 5231 duplicates available in the entire dataset. hence, eliminating it

In [12]:
data = data.drop_duplicates()
duplicate(data)

0

Filtering the country only UK since the question asked in the assesment is, Split the data according to the region of transaction (creation of a basket)

In [13]:
UK = (data[data['Country']== 'United Kingdom']).groupby(['InvoiceNo','Description'])['Quantity'].sum().unstack().reset_index().fillna(0).set_index('InvoiceNo')
GER= (data[data['Country']== 'Germany']).groupby(['InvoiceNo','Description'])['Quantity'].sum().unstack().reset_index().fillna(0).set_index('InvoiceNo')
FR = (data[data['Country']== 'France']).groupby(['InvoiceNo','Description'])['Quantity'].sum().unstack().reset_index().fillna(0).set_index('InvoiceNo')
In [14]:
UK.head()
Out[14]:
Description *Boombox Ipod Classic *USB Office Mirror Ball 10 COLOUR SPACEBOY PEN 12 COLOURED PARTY BALLOONS 12 DAISY PEGS IN WOOD BOX 12 EGG HOUSE PAINTED WOOD 12 HANGING EGGS HAND PAINTED 12 IVORY ROSE PEG PLACE SETTINGS 12 MESSAGE CARDS WITH ENVELOPES 12 PENCIL SMALL TUBE WOODLAND ... returned taig adjust test to push order througha s stock was website fixed wrongly coded 20713 wrongly coded 23343 wrongly marked wrongly marked 23343 wrongly sold (22719) barcode
InvoiceNo
536365 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
536366 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
536367 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
536368 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
536369 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5 rows × 4044 columns

In [15]:
def encode_values(x):
    if x<=0:
        return 0
    if x>=1:
        return 1
    
UK_basket = UK.applymap(encode_values)
GER_basket = GER.applymap(encode_values)
FR_basket = FR.applymap(encode_values)
In [16]:
UK_basket_filter = UK_basket[(UK_basket>0).sum(axis =1)>=2]
GER_basket_filter = GER_basket[(GER_basket>0).sum(axis =1)>=2]
FR_basket_filter = FR_basket[(FR_basket>0).sum(axis =1)>=2]
In [17]:
UK_frequency = apriori(UK_basket_filter, min_support = 0.03, use_colnames = True).sort_values('support', ascending ='False').reset_index(drop = True)
RULES_UK = association_rules(UK_frequency, metric ='lift', min_threshold =1).sort_values('lift', ascending = False).reset_index(drop = True)
RULES_UK
Out[17]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction
0 (PINK REGENCY TEACUP AND SAUCER) (GREEN REGENCY TEACUP AND SAUCER) 0.042284 0.056500 0.034904 0.825465 14.610023 0.032515 5.405792
1 (GREEN REGENCY TEACUP AND SAUCER) (PINK REGENCY TEACUP AND SAUCER) 0.056500 0.042284 0.034904 0.617773 14.610023 0.032515 2.505621
2 (PINK REGENCY TEACUP AND SAUCER) (ROSES REGENCY TEACUP AND SAUCER) 0.042284 0.057710 0.033029 0.781116 13.535248 0.030589 4.304973
3 (ROSES REGENCY TEACUP AND SAUCER) (PINK REGENCY TEACUP AND SAUCER) 0.057710 0.042284 0.033029 0.572327 13.535248 0.030589 2.239365
4 (GARDENERS KNEELING PAD CUP OF TEA) (GARDENERS KNEELING PAD KEEP CALM) 0.045309 0.054262 0.032726 0.722296 13.311351 0.030268 3.405567
5 (GARDENERS KNEELING PAD KEEP CALM) (GARDENERS KNEELING PAD CUP OF TEA) 0.054262 0.045309 0.032726 0.603122 13.311351 0.030268 2.405500
6 (GREEN REGENCY TEACUP AND SAUCER) (ROSES REGENCY TEACUP AND SAUCER) 0.056500 0.057710 0.042405 0.750535 13.005345 0.039145 3.777249
7 (ROSES REGENCY TEACUP AND SAUCER) (GREEN REGENCY TEACUP AND SAUCER) 0.057710 0.056500 0.042405 0.734801 13.005345 0.039145 3.557704
8 (ALARM CLOCK BAKELIKE RED) (ALARM CLOCK BAKELIKE GREEN) 0.056197 0.052689 0.034057 0.606028 11.502008 0.031096 2.404514
9 (ALARM CLOCK BAKELIKE GREEN) (ALARM CLOCK BAKELIKE RED) 0.052689 0.056197 0.034057 0.646383 11.502008 0.031096 2.669000
10 (PAPER CHAIN KIT 50'S CHRISTMAS) (PAPER CHAIN KIT VINTAGE CHRISTMAS) 0.067631 0.048152 0.032545 0.481216 9.993705 0.029288 1.834769
11 (PAPER CHAIN KIT VINTAGE CHRISTMAS) (PAPER CHAIN KIT 50'S CHRISTMAS) 0.048152 0.067631 0.032545 0.675879 9.993705 0.029288 2.876613
12 (WOODEN FRAME ANTIQUE WHITE) (WOODEN PICTURE FRAME WHITE FINISH) 0.056863 0.064969 0.031940 0.561702 8.645715 0.028246 2.133324
13 (WOODEN PICTURE FRAME WHITE FINISH) (WOODEN FRAME ANTIQUE WHITE) 0.064969 0.056863 0.031940 0.491620 8.645715 0.028246 1.855182
14 (LUNCH BAG PINK POLKADOT) (LUNCH BAG BLACK SKULL.) 0.060795 0.073438 0.031577 0.519403 7.072694 0.027112 1.927940
15 (LUNCH BAG BLACK SKULL.) (LUNCH BAG PINK POLKADOT) 0.073438 0.060795 0.031577 0.429984 7.072694 0.027112 1.647681
16 (LUNCH BAG BLACK SKULL.) (LUNCH BAG SUKI DESIGN) 0.073438 0.061763 0.030186 0.411038 6.655110 0.025650 1.593035
17 (LUNCH BAG SUKI DESIGN) (LUNCH BAG BLACK SKULL.) 0.061763 0.073438 0.030186 0.488737 6.655110 0.025650 1.812299
18 (JUMBO STORAGE BAG SUKI) (JUMBO SHOPPER VINTAGE RED PAISLEY) 0.068356 0.068477 0.031033 0.453982 6.629666 0.026352 1.706030
19 (JUMBO SHOPPER VINTAGE RED PAISLEY) (JUMBO STORAGE BAG SUKI) 0.068477 0.068356 0.031033 0.453180 6.629666 0.026352 1.703749
20 (LUNCH BAG PINK POLKADOT) (LUNCH BAG RED RETROSPOT) 0.060795 0.084205 0.033513 0.551244 6.546416 0.028394 2.040740
21 (LUNCH BAG RED RETROSPOT) (LUNCH BAG PINK POLKADOT) 0.084205 0.060795 0.033513 0.397989 6.546416 0.028394 1.560112
22 (LUNCH BAG CARS BLUE) (LUNCH BAG BLACK SKULL.) 0.064364 0.073438 0.030428 0.472744 6.437345 0.025701 1.757330
23 (LUNCH BAG BLACK SKULL.) (LUNCH BAG CARS BLUE) 0.073438 0.064364 0.030428 0.414333 6.437345 0.025701 1.597556
24 (JUMBO BAG PINK POLKADOT) (JUMBO STORAGE BAG SUKI) 0.069990 0.068356 0.030549 0.436474 6.385262 0.025764 1.653239
25 (JUMBO STORAGE BAG SUKI) (JUMBO BAG PINK POLKADOT) 0.068356 0.069990 0.030549 0.446903 6.385262 0.025764 1.681459
26 (LUNCH BAG RED RETROSPOT) (LUNCH BAG SUKI DESIGN) 0.084205 0.061763 0.031214 0.370690 6.001832 0.026013 1.490898
27 (LUNCH BAG SUKI DESIGN) (LUNCH BAG RED RETROSPOT) 0.061763 0.084205 0.031214 0.505387 6.001832 0.026013 1.851537
28 (LUNCH BAG RED RETROSPOT) (LUNCH BAG BLACK SKULL.) 0.084205 0.073438 0.036719 0.436063 5.937859 0.030535 1.643025
29 (LUNCH BAG BLACK SKULL.) (LUNCH BAG RED RETROSPOT) 0.073438 0.084205 0.036719 0.500000 5.937859 0.030535 1.831589
30 (JUMBO BAG RED RETROSPOT) (JUMBO BAG PINK POLKADOT) 0.116327 0.069990 0.047487 0.408216 5.832519 0.039345 1.571538
31 (JUMBO BAG PINK POLKADOT) (JUMBO BAG RED RETROSPOT) 0.069990 0.116327 0.047487 0.678479 5.832519 0.039345 2.748413
32 (LUNCH BAG SPACEBOY DESIGN) (LUNCH BAG RED RETROSPOT) 0.062791 0.084205 0.030307 0.482659 5.731922 0.025019 1.770195
33 (LUNCH BAG RED RETROSPOT) (LUNCH BAG SPACEBOY DESIGN) 0.084205 0.062791 0.030307 0.359914 5.731922 0.025019 1.464192
34 (LUNCH BAG CARS BLUE) (LUNCH BAG RED RETROSPOT) 0.064364 0.084205 0.030791 0.478383 5.681147 0.025371 1.755685
35 (LUNCH BAG RED RETROSPOT) (LUNCH BAG CARS BLUE) 0.084205 0.064364 0.030791 0.365661 5.681147 0.025371 1.474978
36 (JUMBO BAG RED RETROSPOT) (JUMBO BAG STRAWBERRY) 0.116327 0.047910 0.031275 0.268851 5.611581 0.025701 1.302183
37 (JUMBO BAG STRAWBERRY) (JUMBO BAG RED RETROSPOT) 0.047910 0.116327 0.031275 0.652778 5.611581 0.025701 2.544979
38 (JUMBO BAG RED RETROSPOT) (JUMBO BAG BAROQUE BLACK WHITE) 0.116327 0.054685 0.034481 0.296412 5.420337 0.028119 1.343563
39 (JUMBO BAG BAROQUE BLACK WHITE) (JUMBO BAG RED RETROSPOT) 0.054685 0.116327 0.034481 0.630531 5.420337 0.028119 2.391738
40 (JUMBO BAG RED RETROSPOT) (JUMBO STORAGE BAG SUKI) 0.116327 0.068356 0.042224 0.362975 5.310028 0.034272 1.462490
41 (JUMBO STORAGE BAG SUKI) (JUMBO BAG RED RETROSPOT) 0.068356 0.116327 0.042224 0.617699 5.310028 0.034272 2.311460
42 (JUMBO SHOPPER VINTAGE RED PAISLEY) (JUMBO BAG RED RETROSPOT) 0.068477 0.116327 0.039744 0.580389 4.989290 0.031778 2.105933
43 (JUMBO BAG RED RETROSPOT) (JUMBO SHOPPER VINTAGE RED PAISLEY) 0.116327 0.068477 0.039744 0.341654 4.989290 0.031778 1.414943
44 (JUMBO BAG APPLES) (JUMBO BAG RED RETROSPOT) 0.053536 0.116327 0.030730 0.574011 4.934467 0.024502 2.074405
45 (JUMBO BAG RED RETROSPOT) (JUMBO BAG APPLES) 0.116327 0.053536 0.030730 0.264171 4.934467 0.024502 1.286255
46 (JUMBO BAG RED RETROSPOT) (LUNCH BAG RED RETROSPOT) 0.116327 0.084205 0.032121 0.276131 3.279255 0.022326 1.265139
47 (LUNCH BAG RED RETROSPOT) (JUMBO BAG RED RETROSPOT) 0.084205 0.116327 0.032121 0.381466 3.279255 0.022326 1.428656
In [18]:
pivot = RULES_UK.pivot(index = 'consequents', columns = 'antecedents', values= 'lift')

# Generate a heatmap with annotations on and the colorbar off

sns.heatmap(pivot, annot = True, cbar=False)
plt.yticks(rotation=0)
plt.xticks(rotation=90)
plt.show()
In [19]:
frequency_GER = apriori(GER_basket_filter, min_support = 0.03, use_colnames = True).sort_values('support', ascending ='False').reset_index(drop = True)
RULES_GER = association_rules(frequency_GER, metric ='lift', min_threshold =1).sort_values('lift', ascending = False).reset_index(drop = True)
RULES_GER
Out[19]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction
0 (SPACEBOY CHILDRENS BOWL) (SPACEBOY CHILDRENS CUP) 0.044496 0.046838 0.039813 0.894737 19.102632 0.037729 9.055035
1 (SPACEBOY CHILDRENS CUP) (SPACEBOY CHILDRENS BOWL) 0.046838 0.044496 0.039813 0.850000 19.102632 0.037729 6.370023
2 (STRAWBERRY CERAMIC TRINKET BOX) (SWEETHEART CERAMIC TRINKET BOX) 0.056206 0.035129 0.030445 0.541667 15.419444 0.028471 2.105174
3 (SWEETHEART CERAMIC TRINKET BOX) (STRAWBERRY CERAMIC TRINKET BOX) 0.035129 0.056206 0.030445 0.866667 15.419444 0.028471 7.078454
4 (SET OF 12 FAIRY CAKE BAKING CASES) (SET OF 12 MINI LOAF BAKING CASES) 0.044496 0.044496 0.030445 0.684211 15.376731 0.028465 3.025761
... ... ... ... ... ... ... ... ... ...
239 (REGENCY CAKESTAND 3 TIER) (PLASTERS IN TIN WOODLAND ANIMALS) 0.147541 0.147541 0.030445 0.206349 1.398589 0.008677 1.074098
240 (6 RIBBONS RUSTIC CHARM) (ROUND SNACK BOXES SET OF4 WOODLAND) 0.110070 0.262295 0.037471 0.340426 1.297872 0.008600 1.118456
241 (ROUND SNACK BOXES SET OF4 WOODLAND) (6 RIBBONS RUSTIC CHARM) 0.262295 0.110070 0.037471 0.142857 1.297872 0.008600 1.038251
242 (REGENCY CAKESTAND 3 TIER) (ROUND SNACK BOXES SET OF4 WOODLAND) 0.147541 0.262295 0.042155 0.285714 1.089286 0.003455 1.032787
243 (ROUND SNACK BOXES SET OF4 WOODLAND) (REGENCY CAKESTAND 3 TIER) 0.262295 0.147541 0.042155 0.160714 1.089286 0.003455 1.015696

244 rows × 9 columns

In [34]:
frequency_FR = apriori(FR_basket_filter, min_support = 0.03, use_colnames = True).sort_values('support', ascending ='False').reset_index(drop = True)
RULES_FR = association_rules(frequency_FR, metric ='lift', min_threshold =1).sort_values('lift', ascending = False).reset_index(drop = True)
RULES_FR
Out[34]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction
0 (DOLLY GIRL CHILDRENS BOWL, SPACEBOY CHILDRENS... (DOLLY GIRL CHILDRENS CUP, SPACEBOY CHILDRENS ... 0.030055 0.035519 0.030055 1.000000 28.153846 0.028987 inf
1 (DOLLY GIRL CHILDRENS CUP, SPACEBOY CHILDRENS ... (DOLLY GIRL CHILDRENS BOWL, SPACEBOY CHILDRENS... 0.035519 0.030055 0.030055 0.846154 28.153846 0.028987 6.304645
2 (PACK OF 20 SKULL PAPER NAPKINS, SET/6 RED SPO... (PACK OF 6 SKULL PAPER PLATES, SET/6 RED SPOTT... 0.032787 0.035519 0.030055 0.916667 25.807692 0.028890 11.573770
3 (PACK OF 6 SKULL PAPER PLATES, SET/6 RED SPOTT... (PACK OF 20 SKULL PAPER NAPKINS, SET/6 RED SPO... 0.035519 0.032787 0.030055 0.846154 25.807692 0.028890 6.286885
4 (PACK OF 20 SKULL PAPER NAPKINS, SET/6 RED SPO... (PACK OF 6 SKULL PAPER PLATES, SET/6 RED SPOTT... 0.032787 0.035519 0.030055 0.916667 25.807692 0.028890 11.573770
... ... ... ... ... ... ... ... ... ...
1813 (RABBIT NIGHT LIGHT) (RED RETROSPOT MINI CASES) 0.196721 0.147541 0.032787 0.166667 1.129630 0.003762 1.022951
1814 (LUNCH BAG RED RETROSPOT) (PLASTERS IN TIN CIRCUS PARADE) 0.163934 0.180328 0.032787 0.200000 1.109091 0.003225 1.024590
1815 (PLASTERS IN TIN CIRCUS PARADE) (LUNCH BAG RED RETROSPOT) 0.180328 0.163934 0.032787 0.181818 1.109091 0.003225 1.021858
1816 (LUNCH BAG RED RETROSPOT) (RABBIT NIGHT LIGHT) 0.163934 0.196721 0.035519 0.216667 1.101389 0.003270 1.025462
1817 (RABBIT NIGHT LIGHT) (LUNCH BAG RED RETROSPOT) 0.196721 0.163934 0.035519 0.180556 1.101389 0.003270 1.020283

1818 rows × 9 columns

In [35]:
RULES_UK[(RULES_UK['support']>= 0.04) & (RULES_UK['confidence']>=0.08)]
Out[35]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction length
6 (GREEN REGENCY TEACUP AND SAUCER) (ROSES REGENCY TEACUP AND SAUCER) 0.056500 0.057710 0.042405 0.750535 13.005345 0.039145 3.777249 1
7 (ROSES REGENCY TEACUP AND SAUCER) (GREEN REGENCY TEACUP AND SAUCER) 0.057710 0.056500 0.042405 0.734801 13.005345 0.039145 3.557704 1
30 (JUMBO BAG RED RETROSPOT) (JUMBO BAG PINK POLKADOT) 0.116327 0.069990 0.047487 0.408216 5.832519 0.039345 1.571538 1
31 (JUMBO BAG PINK POLKADOT) (JUMBO BAG RED RETROSPOT) 0.069990 0.116327 0.047487 0.678479 5.832519 0.039345 2.748413 1
40 (JUMBO BAG RED RETROSPOT) (JUMBO STORAGE BAG SUKI) 0.116327 0.068356 0.042224 0.362975 5.310028 0.034272 1.462490 1
41 (JUMBO STORAGE BAG SUKI) (JUMBO BAG RED RETROSPOT) 0.068356 0.116327 0.042224 0.617699 5.310028 0.034272 2.311460 1
In [36]:
RULES_GER[(RULES_GER['support']>= 0.04) & (RULES_GER['confidence']>=0.08)]
Out[36]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction length
6 (CHILDRENS CUTLERY DOLLY GIRL) (CHILDRENS CUTLERY SPACEBOY) 0.053864 0.051522 0.042155 0.782609 15.189723 0.039379 4.362998 1
7 (CHILDRENS CUTLERY SPACEBOY) (CHILDRENS CUTLERY DOLLY GIRL) 0.051522 0.053864 0.042155 0.818182 15.189723 0.039379 5.203747 1
8 (SET/6 RED SPOTTY PAPER PLATES) (SET/6 RED SPOTTY PAPER CUPS) 0.060890 0.053864 0.049180 0.807692 14.994983 0.045901 4.919906 1
9 (SET/6 RED SPOTTY PAPER CUPS) (SET/6 RED SPOTTY PAPER PLATES) 0.053864 0.060890 0.049180 0.913043 14.994983 0.045901 10.799766 1
46 (WOODLAND CHARLOTTE BAG) (RED RETROSPOT CHARLOTTE BAG) 0.135831 0.074941 0.063232 0.465517 6.211746 0.053052 1.730755 1
... ... ... ... ... ... ... ... ... ... ...
225 (RED TOADSTOOL LED NIGHT LIGHT) (ROUND SNACK BOXES SET OF4 WOODLAND) 0.103044 0.262295 0.049180 0.477273 1.819602 0.022152 1.411262 1
230 (PLASTERS IN TIN SPACEBOY) (ROUND SNACK BOXES SET OF4 WOODLAND) 0.114754 0.262295 0.051522 0.448980 1.711735 0.021423 1.338798 1
231 (ROUND SNACK BOXES SET OF4 WOODLAND) (PLASTERS IN TIN SPACEBOY) 0.262295 0.114754 0.051522 0.196429 1.711735 0.021423 1.101639 1
242 (REGENCY CAKESTAND 3 TIER) (ROUND SNACK BOXES SET OF4 WOODLAND) 0.147541 0.262295 0.042155 0.285714 1.089286 0.003455 1.032787 1
243 (ROUND SNACK BOXES SET OF4 WOODLAND) (REGENCY CAKESTAND 3 TIER) 0.262295 0.147541 0.042155 0.160714 1.089286 0.003455 1.015696 1

72 rows × 10 columns

In [37]:
RULES_FR[(RULES_FR['support']>= 0.04) & (RULES_FR['confidence']>=0.08)]
Out[37]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction
162 (DOLLY GIRL CHILDRENS BOWL) (DOLLY GIRL CHILDRENS CUP) 0.049180 0.043716 0.040984 0.833333 19.062500 0.038834 5.737705
163 (DOLLY GIRL CHILDRENS CUP) (DOLLY GIRL CHILDRENS BOWL) 0.043716 0.049180 0.040984 0.937500 19.062500 0.038834 15.213115
286 (DOLLY GIRL CHILDRENS BOWL) (SPACEBOY CHILDRENS BOWL) 0.049180 0.054645 0.043716 0.888889 16.266667 0.041028 8.508197
287 (SPACEBOY CHILDRENS BOWL) (DOLLY GIRL CHILDRENS BOWL) 0.054645 0.049180 0.043716 0.800000 16.266667 0.041028 4.754098
306 (PACK OF 6 SKULL PAPER PLATES) (PACK OF 6 SKULL PAPER CUPS, PACK OF 20 SKULL ... 0.060109 0.043716 0.040984 0.681818 15.596591 0.038356 3.005464
... ... ... ... ... ... ... ... ... ...
1797 (PLASTERS IN TIN CIRCUS PARADE) (RABBIT NIGHT LIGHT) 0.180328 0.196721 0.043716 0.242424 1.232323 0.008242 1.060328
1798 (ROUND SNACK BOXES SET OF4 WOODLAND) (RABBIT NIGHT LIGHT) 0.169399 0.196721 0.040984 0.241935 1.229839 0.007659 1.059644
1799 (RABBIT NIGHT LIGHT) (ROUND SNACK BOXES SET OF4 WOODLAND) 0.196721 0.169399 0.040984 0.208333 1.229839 0.007659 1.049180
1810 (PLASTERS IN TIN WOODLAND ANIMALS) (RABBIT NIGHT LIGHT) 0.183060 0.196721 0.040984 0.223881 1.138060 0.004972 1.034994
1811 (RABBIT NIGHT LIGHT) (PLASTERS IN TIN WOODLAND ANIMALS) 0.196721 0.183060 0.040984 0.208333 1.138060 0.004972 1.031924

286 rows × 9 columns

In [38]:
RULES_UK['length']= RULES_UK['antecedents'].apply(lambda x:len(x))
RULES_UK[RULES_UK['length']>1].sort_values('lift',ascending = False)
Out[38]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction length
In [39]:
RULES_FR['length']= RULES_FR['antecedents'].apply(lambda x:len(x))

RULES_FR[RULES_FR['length']>1].sort_values('lift',ascending = False).head()
Out[39]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction length
0 (DOLLY GIRL CHILDRENS BOWL, SPACEBOY CHILDRENS... (DOLLY GIRL CHILDRENS CUP, SPACEBOY CHILDRENS ... 0.030055 0.035519 0.030055 1.000000 28.153846 0.028987 inf 2
1 (DOLLY GIRL CHILDRENS CUP, SPACEBOY CHILDRENS ... (DOLLY GIRL CHILDRENS BOWL, SPACEBOY CHILDRENS... 0.035519 0.030055 0.030055 0.846154 28.153846 0.028987 6.304645 2
2 (PACK OF 20 SKULL PAPER NAPKINS, SET/6 RED SPO... (PACK OF 6 SKULL PAPER PLATES, SET/6 RED SPOTT... 0.032787 0.035519 0.030055 0.916667 25.807692 0.028890 11.573770 3
5 (PACK OF 6 SKULL PAPER PLATES, SET/6 RED SPOTT... (PACK OF 20 SKULL PAPER NAPKINS, SET/6 RED SPO... 0.035519 0.032787 0.030055 0.846154 25.807692 0.028890 6.286885 3
3 (PACK OF 6 SKULL PAPER PLATES, SET/6 RED SPOTT... (PACK OF 20 SKULL PAPER NAPKINS, SET/6 RED SPO... 0.035519 0.032787 0.030055 0.846154 25.807692 0.028890 6.286885 3
In [40]:
RULES_GER['length']= RULES_GER['antecedents'].apply(lambda x:len(x))

RULES_GER[RULES_GER['length']>1].sort_values('lift',ascending = False)
Out[40]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction length
34 (ROUND SNACK BOXES SET OF4 WOODLAND, RED RETRO... (WOODLAND CHARLOTTE BAG) 0.032787 0.135831 0.032787 1.000000 7.362069 0.028333 inf 2
43 (ROUND SNACK BOXES SET OF4 WOODLAND, WOODLAND ... (RED RETROSPOT CHARLOTTE BAG) 0.067916 0.074941 0.032787 0.482759 6.441810 0.027697 1.788447 2
56 (ROUND SNACK BOXES SET OF4 WOODLAND, PLASTERS ... (PLASTERS IN TIN WOODLAND ANIMALS) 0.051522 0.147541 0.037471 0.727273 4.929293 0.029869 3.125683 2
57 (PLASTERS IN TIN CIRCUS PARADE, PLASTERS IN TI... (PLASTERS IN TIN WOODLAND ANIMALS) 0.051522 0.147541 0.037471 0.727273 4.929293 0.029869 3.125683 2
60 (ROUND SNACK BOXES SET OF4 WOODLAND, LUNCH BOX... (ROUND SNACK BOXES SET OF 4 FRUITS) 0.037471 0.168618 0.030445 0.812500 4.818576 0.024127 4.434036 2
63 (ROUND SNACK BOXES SET OF 4 FRUITS, PLASTERS I... (PLASTERS IN TIN CIRCUS PARADE) 0.051522 0.124122 0.030445 0.590909 4.760720 0.024050 2.141036 2
64 (PLASTERS IN TIN CIRCUS PARADE, ROUND SNACK BO... (PLASTERS IN TIN WOODLAND ANIMALS) 0.060890 0.147541 0.042155 0.692308 4.692308 0.033171 2.770492 2
67 (CHARLOTTE BAG APPLES DESIGN, ROUND SNACK BOXE... (ROUND SNACK BOXES SET OF 4 FRUITS) 0.042155 0.168618 0.032787 0.777778 4.612654 0.025679 3.741218 2
69 (PLASTERS IN TIN SPACEBOY, PLASTERS IN TIN WOO... (PLASTERS IN TIN CIRCUS PARADE) 0.065574 0.124122 0.037471 0.571429 4.603774 0.029332 2.043716 2
72 (PLASTERS IN TIN CIRCUS PARADE, PLASTERS IN TI... (PLASTERS IN TIN SPACEBOY) 0.072600 0.114754 0.037471 0.516129 4.497696 0.029140 1.829508 2
74 (PLASTERS IN TIN CIRCUS PARADE, ROUND SNACK BO... (ROUND SNACK BOXES SET OF 4 FRUITS) 0.060890 0.168618 0.044496 0.730769 4.333868 0.034229 3.087989 2
76 (ROUND SNACK BOXES SET OF4 WOODLAND, PLASTERS ... (PLASTERS IN TIN CIRCUS PARADE) 0.079625 0.124122 0.042155 0.529412 4.265261 0.032271 1.861241 2
78 (ROUND SNACK BOXES SET OF4 WOODLAND, PLASTERS ... (PLASTERS IN TIN SPACEBOY) 0.079625 0.114754 0.037471 0.470588 4.100840 0.028333 1.672131 2
85 (ROUND SNACK BOXES SET OF 4 FRUITS, PLASTERS I... (PLASTERS IN TIN WOODLAND ANIMALS) 0.053864 0.147541 0.030445 0.565217 3.830918 0.022498 1.960656 2
91 (ROUND SNACK BOXES SET OF4 WOODLAND, WOODLAND ... (ROUND SNACK BOXES SET OF 4 FRUITS) 0.067916 0.168618 0.042155 0.620690 3.681034 0.030703 2.191825 2
94 (ROUND SNACK BOXES SET OF 4 FRUITS, SPACEBOY L... (ROUND SNACK BOXES SET OF4 WOODLAND) 0.042155 0.262295 0.039813 0.944444 3.600694 0.028756 13.278689 2
99 (CHARLOTTE BAG APPLES DESIGN, ROUND SNACK BOXE... (ROUND SNACK BOXES SET OF4 WOODLAND) 0.035129 0.262295 0.032787 0.933333 3.558333 0.023573 11.065574 2
102 (ROUND SNACK BOXES SET OF 4 FRUITS, ROUND SNAC... (CHARLOTTE BAG APPLES DESIGN) 0.140515 0.070258 0.032787 0.233333 3.321111 0.022915 1.212707 2
104 (ROUND SNACK BOXES SET OF4 WOODLAND, PLASTERS ... (ROUND SNACK BOXES SET OF 4 FRUITS) 0.079625 0.168618 0.044496 0.558824 3.314134 0.031070 1.884465 2
106 (ROUND SNACK BOXES SET OF 4 FRUITS, LUNCH BOX ... (ROUND SNACK BOXES SET OF4 WOODLAND) 0.035129 0.262295 0.030445 0.866667 3.304167 0.021231 5.532787 2
109 (ROUND SNACK BOXES SET OF 4 FRUITS, PLASTERS I... (ROUND SNACK BOXES SET OF4 WOODLAND) 0.051522 0.262295 0.044496 0.863636 3.292614 0.030982 5.409836 2
111 (ROUND SNACK BOXES SET OF 4 FRUITS, WOODLAND C... (ROUND SNACK BOXES SET OF4 WOODLAND) 0.049180 0.262295 0.042155 0.857143 3.267857 0.029255 5.163934 2
114 (ROUND SNACK BOXES SET OF 4 FRUITS, ROUND SNAC... (LUNCH BOX WITH CUTLERY RETROSPOT) 0.140515 0.067916 0.030445 0.216667 3.190230 0.020902 1.189895 2
119 (ROUND SNACK BOXES SET OF4 WOODLAND, SPACEBOY ... (ROUND SNACK BOXES SET OF 4 FRUITS) 0.074941 0.168618 0.039813 0.531250 3.150608 0.027176 1.773614 2
121 (ROUND SNACK BOXES SET OF 4 FRUITS, PLASTERS I... (ROUND SNACK BOXES SET OF4 WOODLAND) 0.053864 0.262295 0.044496 0.826087 3.149457 0.030368 4.241803 2
144 (ROUND SNACK BOXES SET OF 4 FRUITS, ROUND SNAC... (SPACEBOY LUNCH BOX) 0.140515 0.110070 0.039813 0.283333 2.574113 0.024346 1.241762 2
148 (ROUND SNACK BOXES SET OF 4 FRUITS, ROUND SNAC... (PLASTERS IN TIN CIRCUS PARADE) 0.140515 0.124122 0.044496 0.316667 2.551258 0.027055 1.281773 2
150 (PLASTERS IN TIN CIRCUS PARADE, PLASTERS IN TI... (ROUND SNACK BOXES SET OF 4 FRUITS) 0.072600 0.168618 0.030445 0.419355 2.487007 0.018203 1.431824 2
174 (PLASTERS IN TIN CIRCUS PARADE, PLASTERS IN TI... (ROUND SNACK BOXES SET OF4 WOODLAND) 0.072600 0.262295 0.042155 0.580645 2.213710 0.023112 1.759142 2
177 (ROUND SNACK BOXES SET OF 4 FRUITS, ROUND SNAC... (WOODLAND CHARLOTTE BAG) 0.140515 0.135831 0.042155 0.300000 2.208621 0.023068 1.234527 2
183 (PLASTERS IN TIN SPACEBOY, PLASTERS IN TIN WOO... (ROUND SNACK BOXES SET OF4 WOODLAND) 0.065574 0.262295 0.037471 0.571429 2.178571 0.020271 1.721311 2
191 (ROUND SNACK BOXES SET OF 4 FRUITS, ROUND SNAC... (PLASTERS IN TIN WOODLAND ANIMALS) 0.140515 0.147541 0.044496 0.316667 2.146296 0.023765 1.247501 2
209 (WOODLAND CHARLOTTE BAG, RED RETROSPOT CHARLOT... (ROUND SNACK BOXES SET OF4 WOODLAND) 0.063232 0.262295 0.032787 0.518519 1.976852 0.016201 1.532156 2
In [ ]:
In [ ]: