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Amazon-Fine-Food-Reviews-

Performed Exploratory Data Analysis, Data Cleaning, Data Visualization and Text Featurization(BOW, tfidf, Word2Vec). Build several ML models like KNN, Naive Bayes, Logistic Regression, SVM

Objective

Given a text review, determine the sentiment of the review whether its positive or negative. Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews

About Dataset

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

Outcomes

Learnt performing

  • Exploratory data analysis
  • T-sne
  • Sentiment analysis
  • Featurizations such as Bag of Words, Tfidf and Wor2Vec and text processing.

ML Algorithms Used:

  • KNN
  • Naive Bayes
  • Logistic Regression
  • Support Vector Machine
  • Decision Tree
  • Random Forest and XGBoost

An Enlightening Introduction to Machine Learning providing skills to work on future projects