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 # RF-and-GBDT-using-XGBOOST-on-amazon-food-dataset
 Built Random Forest and GBDT using XGBOOST model on Amazon fine food review dataset
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+Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews 
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+EDA: https://nycdatascience.com/blog/student-works/amazon-fine-foods-visualization/
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+The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.
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+Number of reviews: 568,454
+Number of users: 256,059
+Number of products: 74,258
+Timespan: Oct 1999 - Oct 2012
+Number of Attributes/Columns in data: 10
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+Attribute Information:
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+Id
+ProductId - unique identifier for the product
+UserId - unqiue identifier for the user
+ProfileName
+HelpfulnessNumerator - number of users who found the review helpful
+HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not
+Score - rating between 1 and 5
+Time - timestamp for the review
+Summary - brief summary of the review
+Text - text of the review
+Objective:
+Given a review, determine whether the review is positive (rating of 4 or 5) or negative (rating of 1 or 2).