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