- About the Data:
The Beer Profile and Ratings dataset from Kaggle was used for the project. The main data set (beer_profile_and_ratings.csv) contains the following columns:
(General)
• Name: Beer name (label)
• Style: Beer Style
• Brewery: Brewery name
• Beer Name: Complete beer name (Brewery + Brew Name)
• Description: Notes on the beer if available
• ABV: Alcohol content of beer (% by volume)
• Min IBU: The minimum IBU value each beer can possess
• Max IBU: The maximum IBU value each beer can possess
(Mouth feel)
• Astringency
• Body
• Alcohol
(Taste)
• Bitter
• Sweet
• Sour
• Salty
(Flavor And Aroma)
• Fruits
• Hoppy
• Spices
• Malty
(Reviews)
• review_aroma
• review_appearance
• review_palate
• review_taste
• review_overall
• number_of_reviews
The data columns are a mix of both character and numeric types.
- Objectives and Motivation
The main objectives we hope to accomplish with this project are as follows:
• Understand the influence of the profile parameters on the review parameters
• Find the if there are any parameters that are more important than the others
• Find the relationship between the profile parameters
• Identify the most/least popular styles of beer
As a consumer, knowing what each beer feels like and what other people think about it would make one make better decisions while choosing a drink, or make it easier to explore new options.
As a manufacturer, knowing what the people prefer and what they are talking about the most would help produce better, more desirable products for the market.
Even though this is just a small example, the concepts used here can be used (and improved upon) to do real-world analysis on much larger data as well.