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Given a Amazon fine food review, determine whether the review is positive (Rating of 4 or 5) or negative (rating of 1 or 2).
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03 k-NN on Amazon reviews data-set
04 Naive Bayes on Amazon reviews
05 Logistic Regression on Amazon reviews data set
06 SVM on Amazon reviews data set
07 Decision Trees on Amazon reviews data set
08 Random Forests & GBDT on Amazon reviews data set
Apply t_SNE on Amazon reviews with polarity based color-coding
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README.md

README.md

Amazon-Fine-Food-Reviews

Amazon Fine Food Reviews Analysis Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews

68747470733a2f2f692e696d6775722e636f6d2f6241704235544d2e6a7067

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:

  1. Id
  2. ProductId - unique identifier for the product
  3. UserId - unqiue identifier for the user
  4. ProfileName
  5. HelpfulnessNumerator - number of users who found the review helpful
  6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not
  7. Score - rating between 1 and 5
  8. Time - timestamp for the review
  9. Summary - brief summary of the review 10)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).

  1. TSNE
  2. KNN
  3. Naive Bayes
  4. Logistic Regression
  5. Support Vector Machine
  6. Decision Tree
  7. Random Forest and XGBoost
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