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Type: User
Type: User
We have chosen Amazon product sales data set comprising of sales activity and user ratings for each product. The idea is to create a product suggestion/recommendation system for each user based on his previous purchases and his rating for each one. A Collaborative Filtering model is built to predict the virtual ratings for the product that the user did not purchase. The system predicts the user rating for all the items and we display the products which user may be like, buy and rate higher.
Building Recommendation Model for the electronics products of Amazon
You would have probably come across the Recommendations by YouTube, or Netflix or even on Amazon when purchasing products. So this is a Recommmendation System which creates a Recommendation system based on a Big Mart Sales of products using Apriori and Eclat Machine Learning models.
This is a repository of a topic-centric public data sources in high quality for Recommender Systems (RS)
Python script demonstrating spark streaming and Kafka implementation using an e-commerce website like product recommendation engine based on item-based collaborative filtering. 🐍. 💥
Code used in Kaggle's Santander Product Recommendation competition.
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