The following data set is from Kaggle. The data set is called “coffee ratings” and it includes reviews of different coffee beans around the world. This was the first data set the popped up so that is why I chose it. The data provides adjectives of the coffee with simple phrases and sentences to describe the coffee. This will be the first 100 observations so we can better keep track of the data. This is the original data set https://www.kaggle.com/datasets/schmoyote/coffee-reviews-dataset. Overall I also enjoy coffee so this topic was the most interesting to me. All of the descriptions are very detailed as well so it makes it very easy too review the coffee with the description.
data <- read.csv(“coffee_analysis.csv”)
summary(data)
model <- lm(Flavor ~ Aroma, data = data) summary(model)
plot(data\(Aroma, data\)Flavor) abline(model, col = “blue”)
The regression analysis that was ran showed that the smell of coffee have a very strong correlation to flavor ratings. This means that customers rate coffee better when the smell of the coffee shop is better. For data analysis purposes this incite can help the coffee shops better emphasis small quality such as aroma for specific store and specific locations. These coffee shops can focus more into small details such as there to provide a better overall experience for the customer. Or even is locations where there are not enough sales they can increase the sales by focusing on the little details such as the aroma of the coffee shop.