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Recommender System Machine Learning Project for Beginners - Learn how to design, implement and train a rule-based recommender system in Python
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Business Overview
A recommendation system is a type of algorithm designed to recommend or suggest things to the user based on many different factors. The recommendation system deals with a large amount of data and filters it out based on user’s preferences and interests. With the rise of Youtube, Netflix, Amazon, etc., recommendation systems have taken a crucial place. Recommender systems are critical in many industries as they can help to generate a large amount of revenue. This series of projects aims to introduce recommendation systems and several algorithms used for recommendation.
This project mainly focuses on the basics of the recommendation system and a brief introduction to the different algorithms. Implementation of a Rule-based recommendation system has also been covered.
Aim
To understand the basics of a recommendation system and build a rule-based recommender system.
Data Description
The dataset is a transnational data set containing all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts with maximum wholesaler customers. The dataset can be found at The UCL machine learning repository. The dataset contains information about 541910 customers over eight attributes. The eight attributes are InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country.
Tech Stack
Language: Python
Libraries: pandas, numpy, seaborn, matplotlib
Approach
Data Description
Exploratory Data Analysis
Data Cleaning
Rule-based Recommendation system
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