Amazon_Vine_Analysis

Project Overview

Assist the client to determine if there is any bias toward favorable reviews from Vine members. I did so by picking a dataset from a set list and use PySpark to perform the ETL process to extract the dataset, transform the data, connect to an AWS RDS instance, and load the data into pgAdmin.

Methodology

Tools/Programs/Languages used:

• Google Colab
• Python / PySpark
• AWS RDS
• ETL operations
• SQL
• pgAdmin
• Excel

• I used PySpark to import data from an S3 bucket and read it as a dataframe.

• Then, I cleaned up the data and filtered the data by the amount of reviews and stars given by Amazon Vine Members and Non-Amazon Vine Members.
• This led me to create 3 different tables.

• Afterwards, I connected to an AWS RDS (cloud database) instance and added each "clean" DataFrame to its corresponding table.

• Once I confirmed connection to the cloud database, I then linked AWS to my database in pgAdmin. I used basic SQL queries to make sure the data loaded correctly.

Summary of Results

• How many Vine reviews and non-Vine reviews were there?

  • Total Vine Reviews = 613
  • Total non-Vine Reviews = 64,968

• How many Vine reviews were 5 stars? How many non-Vine reviews were 5 stars?

  • Total Vine 5 star reviews = 222
  • Total non-Vine 5 star Reviews = 30,543

• What percentage of Vine reviews were 5 stars? What percentage of non-Vine reviews were 5 stars?

  • Percentage Vine 5 star reviews = 0.7%
  • Percentage non-Vine 5 star reviews = 99.3%

Conclusion

• I would conclude that there is no positivity bias for reviews in the Vine program as they are such a small percentage of the total reviews.
• Only about a third of the total vine reviews had 5 star reviews.