WebScraping Data in Public Goods

Posted on Feb 22, 2021
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Introduction

For my web scraping project, I chose to scrape product information from Public Goods, a membership-based online home goods store with a focus on quality, sustainability, and simplicity. Β My main objective was to analyze the scraped product data to discover trends in price and ratings across product categories – ranging from Personal Care, Household, Grocery, Supplement & Vitamins, Pets, and CBD. In analyzing product data, I sought to not only discover if price points played a factor in customer ratings and reviews, but also what categories yielded the most engagement.

Β 

Scraped Data

Using python to build my Selenium web scraper, I crawled through 250 product pages on Public Good’s website. Information that I specifically targeted was the product’s name, product description, core features, main ingredients, price, volume, ratings, and number of reviews. With python I was able to do the bulk of my preprocessing and created a pandas dataframe containing the main variables I wanted to analyze. Once creating the dataframe I used Seaborn to visualize the data, and create the plots showcased in this blog.


Data Outcomes

WebScraping Data in Public Goods

  • In some cases, the lower the price of the product, the higher the rating. This is not true across all categories.

WebScraping Data in Public Goods

  • Number of reviews may be affected by price point, but there are other features to consider.

WebScraping Data in Public Goods

  • Star ratings from 3 – 4 have a lower range of number of reviews. Whereas star ratings from 4 -5 have a more inclusive range.

  • The highest engagement in regards to star ratings and number of reviews were found in the grocery and household product category.Β 

Next StepsΒ 

  • Investigating if variables such as: core features and main ingredients influence star ratings and reviews.
  • Text Sentiment Analysis / NLP
  • Acquiring revenue data.
  • Integrating competitor data – i.e Brandless, Amazon Prime, etc.
  • Acquiring actual data on how negative reviews effect business revenue and churn.

 

About Author

Juan R. Vasquez Jr.

Juan is a recent graduate of NYC Data Science Academy where he studied dashboard creation, machine learning, and statistical analysis. His background of three years in the hospitality and commercial art industry allowed him to hone his organization...
View all posts by Juan R. Vasquez Jr. >

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