Untitled

Practical 1
Aim: Association Rules(Apriori Algorithm)
[] pip install apyori
Collecting apyori
Downloading apyori-1.1.2.tar.gz (8.6 kB) Preparing metadata (setup.py) ... done
Building wheels for collected packages: apyori
Building wheel for apyori (setup.py)... done
Created wheel for apyori: filename=apyori-1.1.2-py3-none-any.whl size=5954 sha256-1d Stored in directory: /root/.cache/pip/wheels/c4/1a/79/20f55c470a50bb3702a8cb7c94d8ad. Successfully built apyori
Installing collected packages: apyori
Successfully installed apyori-1.1.2
[ ] import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from apyori import apriori
df=pd.read_csv("/content/Market_Basket Optimisation - Market_Basket_Optimisation.csv")
⇒
df.head()
shrimp almonds avocado
vegetables
green mix grapes
whole weat yams flour
energy cottage tomato low fat cheese drink juice yogurt
gr
0
burgers meatballs eggs
NaN
NaN
NaN NaN
NaN
NaN
NaN
NaN
1 chutney
NaN
NaN
NaN
NaN
NaN NaN
NaN
NaN
NaN
NaN
2
turkey avocado
NaN
NaN
NaN
NaN NaN
NaN
NaN
NaN
NaN
↑
mineral
3
milk
energy whole wheat
green
NaN NaN
NaN
NaN
NaN
NaN
water
bar
rice
tea
4
low fat yogurt
NaN
NaN
NaN
NaN
NaN NaN
NaN
NaN
NaN
NaN
transactions=[]
for i in range(0,7500):
transactions.append([str(df.values[i, j]) for j in range(0,20)])
print(transactions)
turkey', 'avocado', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',
rules-apriori (transactions-transactions,min_support=0.003,min_confidence-0.4,min_lift-4,min_length=3,max_length=3)
[] results=list(rules)
print(results)
[RelationRecord(items-frozenset({'cereals', 'spaghetti', 'ground beef'}), support-8.0030666666666666668, ordered_statu)]