Recommendation-Engine

Introduction

This repo contains my first hands-on experience in developing a Recommendation engine using IBM Watson Studio dataset. The goal is to recommend the articles to the user using various types of Recommendation engines that I studied while pursuing my Data Science Nanodegree from Udacity.

Contents

• I. Exploratory Data Analysis
• II. Rank Based Recommendations
• III. User-User Based Collaborative Filtering
• IV. Matrix Factorization
• V. Extras & Concluding

Description of Files

• I. Recommendation Engine - Contains the Python notebook of the whole project
• II. data - Contains two CSV files: - articles_community.csv - contains 1056 rows and 5 columns namely doc_body, doc_description, doc_full_name, doc_status, and article_id. - user_item_interactions.csv - contains 45,993 rows and 3 columns namely article ids, title, and email. Basically, gives info about which user is interacting with which article

How to use

Clone the repository, download the data folder, Python notebook named 'Recommendations_with_IBM.ipynb', and simply run it.