The recommendation system deals with a large amount of data and filters it out based on the user's preferences and interests. With the rise of platforms like YouTube, Netflix, and Amazon, recommendation systems have become crucial. They play a significant role in generating revenue.
Market Basket Analysis, also known as Association Analysis, helps retailers understand customer purchasing patterns based on historical data. It allows retailers to identify relationships between the items people buy.
- Understand the basics of a recommendation system and build a rule-based recommender system.
- Perform Exploratory Data Analysis and implement Market Basket Analysis.
The dataset covers all transactions between 01/12/2010 and 09/12/2011 for a UK-based online retail company. The company specializes in unique all-occasion gifts with a focus on wholesale customers.
It includes information on 541,910 customers with eight attributes: InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country.
You can find the dataset at The UCL machine learning repository.
- Language: Python
- Libraries: pandas, numpy, seaborn, matplotlib, collections, mlxtend, wordcloud, networkx
- Data Description
- Exploratory Data Analysis
- Data Cleaning
- Rule-Based Recommendation System
- Popular items globally
- Popular items by country
- Popular items by month
- Repurchase recommendations