Coffee Makers Customer Review Analysis

Jacqueline Hsu, Kelly Wang

Inspiration

Both team members are coffee drinkers, so we decided to investigate a topic that could benefit us in the future. We also wanted to utilize this opportunity to learn a new Python tool.

What it does

We web scraped customer reviews from Amazon for coffee makers by Nespresso, Keurig, and Mr. Coffee.

How we built it

We built a web scraping framework that could be applied to all three products.

Challenges we ran into

Data manipulation and visualization creation took longer than we expected.

Accomplishments that we're proud of

We managed to learn a new library (BeautifulSoup) in less than a week and build a working model.

What we learned

From a technical standpoint, we learned how to use BeautifulSoup to scrape data off the web. About our project specifically, we learned about customers' preferences and shopping behavior.

What's next for Coffee Makers Customer Review Analysis

• Research into more coffee makers
• Extend our web scraping learnings to other products
• Use TF-IDF (Term Frequency — Inverse Document Frequency) to evaluate how important a specific word is
• Develop machine learning models and use natural language processing (NLP) techniques to classify opinions expressed in a text review (positive or negative)

Built With

• beautiful-soup