Machine learning algorithm such as KNN,Naive Bayes,Logistic Regression,SVM,Decision Trees,Random Forest,k means and Truncated SVD on amazon fine food review
The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.
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
Attribute Information:
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)
With the perception of text/review we predicted the polarity of review.In this project we applied various algorithm such as KNN,Naive Bayes,Logistic Regression,Support Vector machine,Decision trees,Random forest & GBDT ,LSTM .