Market Basket Analysis of Store Data

Dataset Description

• Different products given 7500 transactions over the course of a week at a French retail store.
• We have library(apyori) to calculate the association rule using Apriori.

Import the Library

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from apyori import apriori

Read data and Display

store_data = pd.read_csv("store_data.csv", header=None)
display(store_data.head())
print(store_data.shape)

<div> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>0</th> <th>1</th> <th>2</th> <th>3</th> <th>4</th> <th>5</th> <th>6</th> <th>7</th> <th>8</th> <th>9</th> <th>10</th> <th>11</th> <th>12</th> <th>13</th> <th>14</th> <th>15</th> <th>16</th> <th>17</th> <th>18</th> <th>19</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>shrimp</td> <td>almonds</td> <td>avocado</td> <td>vegetables mix</td> <td>green grapes</td> <td>whole weat flour</td> <td>yams</td> <td>cottage cheese</td> <td>energy drink</td> <td>tomato juice</td> <td>low fat yogurt</td> <td>green tea</td> <td>honey</td> <td>salad</td> <td>mineral water</td> <td>salmon</td> <td>antioxydant juice</td> <td>frozen smoothie</td> <td>spinach</td> <td>olive oil</td> </tr> <tr> <th>1</th> <td>burgers</td> <td>meatballs</td> <td>eggs</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> </tr> <tr> <th>2</th> <td>chutney</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> </tr> <tr> <th>3</th> <td>turkey</td> <td>avocado</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> </tr> <tr> <th>4</th> <td>mineral water</td> <td>milk</td> <td>energy bar</td> <td>whole wheat rice</td> <td>green tea</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> <td>NaN</td> </tr> </tbody> </table> </div>

(7501, 20)

Preprocessing on Data

• Here we need a data in form of list for Apriori Algorithm.

records = []
for i in range(1, 7501):
    records.append([str(store_data.values[i, j]) for j in range(0, 20)])

print(type(records))

<class 'list'>

Apriori Algorithm

• Now time to apply algorithm on data.
• We have provide min_support, min_confidence, min_lift, and min length of sample-set for find rule.

Measure 1: Support.

This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears. In Table 1 below, the support of {apple} is 4 out of 8, or 50%. Itemsets can also contain multiple items. For instance, the support of {apple, beer, rice} is 2 out of 8, or 25%.

If you discover that sales of items beyond a certain proportion tend to have a significant impact on your profits, you might consider using that proportion as your support threshold. You may then identify itemsets with support values above this threshold as significant itemsets.

Measure 2: Confidence.

This says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}. This is measured by the proportion of transactions with item X, in which item Y also appears. In Table 1, the confidence of {apple -> beer} is 3 out of 4, or 75%.

One drawback of the confidence measure is that it might misrepresent the importance of an association. This is because it only accounts for how popular apples are, but not beers. If beers are also very popular in general, there will be a higher chance that a transaction containing apples will also contain beers, thus inflating the confidence measure. To account for the base popularity of both constituent items, we use a third measure called lift.

Measure 3: Lift.

This says how likely item Y is purchased when item X is purchased, while controlling for how popular item Y is. In Table 1, the lift of {apple -> beer} is 1,which implies no association between items. A lift value greater than 1 means that item Y is likely to be bought if item X is bought, while a value less than 1 means that item Y is unlikely to be bought if item X is bought.

association_rules = apriori(records, min_support=0.0045, min_confidence=0.2, min_lift=3, min_length=2)
association_results = list(association_rules)

How many relation derived

print("There are {} Relation derived.".format(len(association_results)))

There are 48 Relation derived.

Association Rules Derived

for i in range(0, len(association_results)):
    print(association_results[i][0])

frozenset({'light cream', 'chicken'})
frozenset({'escalope', 'mushroom cream sauce'})
frozenset({'escalope', 'pasta'})
frozenset({'herb & pepper', 'ground beef'})
frozenset({'tomato sauce', 'ground beef'})
frozenset({'olive oil', 'whole wheat pasta'})
frozenset({'shrimp', 'pasta'})
frozenset({'nan', 'light cream', 'chicken'})
frozenset({'shrimp', 'chocolate', 'frozen vegetables'})
frozenset({'cooking oil', 'spaghetti', 'ground beef'})
frozenset({'escalope', 'mushroom cream sauce', 'nan'})
frozenset({'escalope', 'pasta', 'nan'})
frozenset({'spaghetti', 'ground beef', 'frozen vegetables'})
frozenset({'milk', 'olive oil', 'frozen vegetables'})
frozenset({'shrimp', 'mineral water', 'frozen vegetables'})
frozenset({'spaghetti', 'olive oil', 'frozen vegetables'})
frozenset({'shrimp', 'spaghetti', 'frozen vegetables'})
frozenset({'spaghetti', 'frozen vegetables', 'tomatoes'})
frozenset({'spaghetti', 'ground beef', 'grated cheese'})
frozenset({'herb & pepper', 'ground beef', 'mineral water'})
frozenset({'herb & pepper', 'nan', 'ground beef'})
frozenset({'herb & pepper', 'spaghetti', 'ground beef'})
frozenset({'milk', 'ground beef', 'olive oil'})
frozenset({'nan', 'tomato sauce', 'ground beef'})
frozenset({'shrimp', 'spaghetti', 'ground beef'})
frozenset({'milk', 'spaghetti', 'olive oil'})
frozenset({'soup', 'mineral water', 'olive oil'})
frozenset({'nan', 'olive oil', 'whole wheat pasta'})
frozenset({'shrimp', 'nan', 'pasta'})
frozenset({'spaghetti', 'pancakes', 'olive oil'})
frozenset({'shrimp', 'chocolate', 'frozen vegetables', 'nan'})
frozenset({'cooking oil', 'nan', 'spaghetti', 'ground beef'})
frozenset({'nan', 'spaghetti', 'ground beef', 'frozen vegetables'})
frozenset({'milk', 'spaghetti', 'mineral water', 'frozen vegetables'})
frozenset({'milk', 'nan', 'olive oil', 'frozen vegetables'})
frozenset({'shrimp', 'nan', 'mineral water', 'frozen vegetables'})
frozenset({'nan', 'spaghetti', 'olive oil', 'frozen vegetables'})
frozenset({'shrimp', 'nan', 'spaghetti', 'frozen vegetables'})
frozenset({'nan', 'spaghetti', 'frozen vegetables', 'tomatoes'})
frozenset({'nan', 'spaghetti', 'ground beef', 'grated cheese'})
frozenset({'herb & pepper', 'nan', 'ground beef', 'mineral water'})
frozenset({'herb & pepper', 'nan', 'spaghetti', 'ground beef'})
frozenset({'milk', 'nan', 'ground beef', 'olive oil'})
frozenset({'shrimp', 'nan', 'spaghetti', 'ground beef'})
frozenset({'milk', 'nan', 'spaghetti', 'olive oil'})
frozenset({'nan', 'soup', 'mineral water', 'olive oil'})
frozenset({'nan', 'spaghetti', 'pancakes', 'olive oil'})
frozenset({'milk', 'frozen vegetables', 'nan', 'spaghetti', 'mineral water'})

Rules Generated

for item in association_results:
    # first index of the inner list
    # Contains base item and add item
    pair = item[0]
    items = [x for x in pair]
    print("Rule: " + items[0] + " -> " + items[1])

    # second index of the inner list
    print("Support: " + str(item[1]))

    # third index of the list located at 0th
    # of the third index of the inner list

    print("Confidence: " + str(item[2][0][2]))
    print("Lift: " + str(item[2][0][3]))
    print("=====================================")

Rule: light cream -> chicken
Support: 0.004533333333333334
Confidence: 0.2905982905982906
Lift: 4.843304843304844
=====================================
Rule: escalope -> mushroom cream sauce
Support: 0.005733333333333333
Confidence: 0.30069930069930073
Lift: 3.7903273197390845
==========================