Amazon_Vine_Analysis

Amazon Vine Analysis

This project is part of the Data Analytics and Visualization Bootcamp at Northwestern University.

Project Objective

To analyze Amazon reviews written by members of the paid Amazon Vine Program

Methods Used

Natural Language Processing Machine Learning

Technologies

PySpark AWS RDS Pandas ProgreSQL

Project Description

This project analyzes Amazon reviews written by members of the paid Amazon Vine program. The Amazon Vine program is a service that allows manufacturers and publishers to receive reviews for their products. Companies like SellBy pay a small fee to Amazon and provide products to Amazon Vine members, who are then required to publish a review. In this project, I make use of musical instruments datasets. It contains reviews of musical instruments. I used PySpark to perform the ETL process to extract the dataset, transform the data, connect to an AWS RDS instance, and load the transformed data into pgAdmin. Next, I used PySpark to determine any bias toward favorable reviews from Vine members in the dataset.

Results

• There are 60 Vine reviews and 14477 non-Vine reviews in the datasets.
• 34 were 5 stars out of 60 Vine reviews while 8212 were 5 stars for non-Vine reviews.
• There were 56.67% of 5 star rating reviews of Vine while there were 56.72% of 5 star rating for non-Vine

Summary

The analysis makes use of the paid and unpaid Vine column. There might be a bias of hasty generalization. The verified_purchases column should be used to ascertain that the people who reviewed the products are actual buyers of the product.

Needs of this project

Natural Language Processing/ Cleaning