apriori_association_rule_learning_big_basket_cart_prediction.py

# -*- coding: utf-8 -*-
"""Apriori_Association_Rule_Learning_Big-Basket-Cart-Prediction.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1hFGPfS20Yu9CXKmGov2BvYLklkSldTzo

# Apriori

## Importing libraries
"""

!pip install apyori

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

"""## Importing dataset"""

dataset = pd.read_csv('Big Basket.com Cart.csv', header = None)
transactions = []

for i in range(0, 7219):
  transactions.append([str(dataset.values[i,j]) for j in range(0, 20)])

"""## Apriori Training on Dataset"""

from apyori import apriori
rules = apriori(transactions = transactions, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2, max_length = 2)

"""## Visualizing

### Raw Results
"""

results = list(rules)

results

print(results)

"""### Proper Display"""

def inspect(results):
    product1         = [tuple(result[2][0][0])[0] for result in results]
    product2         = [tuple(result[2][0][1])[0] for result in results]
    supports    = [result[1] for result in results]
    confidences = [result[2][0][2] for result in results]
    lifts       = [result[2][0][3] for result in results]
    return list(zip(product1, product2, supports, confidences, lifts))
DataFrame_intelligence = pd.DataFrame(inspect(results), columns = ['product1', 'product1', 'Support', 'Confidence', 'Lift'])

DataFrame_intelligence

DataFrame_intelligence.nlargest(n = 10, columns = 'Lift')