GitHub / huzaifakhan04 / amazon-product-recommendation-system-web-application-flask-using-mongodb-pyspark-and-apache-kafka
This repository includes a web application connected to a product recommendation system developed with the comprehensive Amazon Review Data (2018) dataset, consisting of nearly 233.1 million records and occupying approximately 128 gigabytes (GB) of data storage, using MongoDB, PySpark, and Apache Kafka.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/huzaifakhan04%2Famazon-product-recommendation-system-web-application-flask-using-mongodb-pyspark-and-apache-kafka
PURL: pkg:github/huzaifakhan04/amazon-product-recommendation-system-web-application-flask-using-mongodb-pyspark-and-apache-kafka
Stars: 0
Forks: 0
Open issues: 0
License: bsd-3-clause
Language: Jupyter Notebook
Size: 91 MB
Dependencies parsed at: Pending
Created at: almost 2 years ago
Updated at: almost 2 years ago
Pushed at: almost 2 years ago
Last synced at: almost 2 years ago
Topics: als, alternating-least-squares, amazon, amazon-reviews, apache-kafka, customer-reviews, flask-application, inferential-statistics, machine-learning, mongodb, nosql, product-recommendations, product-recommender-system, product-reviews, pyspark, spark-mllib, statistical-inference, supervised-learning, web-application