New Dataset

# Dataset Preview




To give you a preview of the Amazon Food Reviews dataset, I’ll provide a brief example of the data structure based on common fields included in the dataset. Here’s what a preview might look like:


Description of Columns

1.Id: A unique identifier for each review.
2.Product Id: The Amazon product ID for the reviewed food item.
3.User Id: A unique identifier for the user who wrote the review.
4.Profile Name: The name of the reviewer.
5.Helpfulness: A ratio showing how many users found the review helpful (e.g., 1/1 = 1 out of 1 users).
6.Score: The rating given by the reviewer, ranging from 1 (worst) to 5 (best).
7.Time: The timestamp when the review was written, usually in Unix epoch format.
8.Text: The detailed review text written by the user.

Key Insights from the Preview

1.Reviews contain both quantitative data (e.g., Score, Helpfulness) and qualitative data (e.g., Summary, Text), making it useful for a wide range of analytical tasks.
2.The Helpfulness column can be used to evaluate the perceived value of each review.
3.The dataset spans different products, as indicated by unique Product Id values.
4.Review text (Text) provides ample data for natural language processing applications like sentiment analysis.

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+Amazon_Food_Reviews.csv filter=lfs diff=lfs merge=lfs -text

Amazon_Food_Reviews.csv

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