https://thagasheriff64-mlp-app-evr9bc.streamlit.app/
Recipe for Rating: Predict Food Ratings using ML Welcome to "Recipe for Rating" an exciting machine learning challenge. Your task is to create models that can accurately predict food rating
In this challenge, your goal is to build models that can guess the ratings for each recipe using given information.
This dataset is your gateway to the Recipe Ratings Prediction Challenge! Each entry captures a unique culinary story with recipe names, user reviews, and key features. Your task is to explore this rich data and develop predictive models that can forecast the ratings for every recipe. Unleash your creativity and analytical skills to unlock the secrets hidden in the world of flavours!
The dataset is composed of the following files:
train.csv: The training set, which includes the target variable 'rating' and accompanying feature attributes.
test.csv: The test set, containing similar feature attributes but without the target variable 'rating', as it is the variable to be predicted.
sample_submission.csv: A sample submission file provided in the correct format for competition submissions.
RecipeNumber: Placement of the recipe on the top 100 recipes list
RecipeCode: Unique ID of the recipe used by the site
RecipeName: Name of the recipe the comment was posted on
CommentID: Unique ID of the comment
UserID: Unique ID of the user who left the comment
UserName: Name of the user
UserReputation: Internal score of the site, roughly quantifying the past behavior of the user
CreationTimestamp: Time at which the comment was posted as a Unix timestamp
ReplyCount: Number of replies to the comment
ThumbsUpCount: Number of up-votes the comment has received
ThumbsDownCount: Number of down-votes the comment has received
Rating: The score on a 1 to 5 scale that the user gave to the recipe. A score of 0 means that no score was given (Target Variable)
BestScore: Score of the comment, likely used by the site to help determine the order comments appear in
Recipe_Review: Text content of the comment