{"repo":"RudrenduPaul/Python-Ecommerce-recommendation-system-using-machine-learning","free":true,"listed":false,"github":"https://github.com/RudrenduPaul/Python-Ecommerce-recommendation-system-using-machine-learning","clone":"git clone https://github.com/RudrenduPaul/Python-Ecommerce-recommendation-system-using-machine-learning.git","description":"Business setting up their recommendation system for first time without any product rating history, & Amazon/Netflix type of recommendation system after the website has collected significant product reviews","language":"Jupyter Notebook","stars":96,"topics":["collaborative-filtering","data-science","ecommerce","jupyter-notebook","kaggle-dataset","machine-learning","python","recommendation-system","scikit-learn","text-clustering"],"license":null,"category":"ecommerce","readme_excerpt":"E-commerce Recommendation System (Python, Amazon & Home Depot datasets) Open Recommendation System - Paul.ipynb for the full analysis and code. A well-designed recommendation system helps businesses improve the shopping experience on their site, which in turn improves customer acquisition and retention. This project builds one in three parts, following a new customer's actual journey: from their first visit with no purchase history, through their first purchase, to repeat visits once ratings and purchase history exist. Datasets 1. Amazon product ratings from multiple users: Kaggle: Amazon Ratings 2. Home Depot product descriptions: Kaggle: Home Depot Product Search Relevance Strategy 1. Popularity-based recommendations for new customers A new customer with no purchase history is shown the most popular products on the site. This is a simple, effective way to cold-start a recommendation engine before any personal signal exists. 2. Model-based collaborative filtering Once a customer has purchase history, recommendations switch to items based on that history and the ratings of other users who bought similar items. A model-based collaborative filtering approach was chosen here because it predicts products for a specific user by learning patterns across many users' preferences at once. 3. Item-item collaborative filtering (cold start for a new business) For a business with no user-item purchase history at all, recommendations can instead come from a search-engine-style system based","default_branch":null,"files":null,"tree":[],"storefront":"/r/RudrenduPaul","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/RudrenduPaul/Python-Ecommerce-recommendation-system-using-machine-learning/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}