Amazon fine food review analysis

This repository is about appying different machine learning techniques on amazon food review dataset.

Below are some data reduction, classification and regression techniques applied on amazon fine food review dataset.

• KNN
• Naive Bayes
• Logistic Regression
• Support Vector Machine
• Decision Tree
• Random Forest
• SGD
• T-SNE

Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews

EDA: https://nycdatascience.com/blog/student-works/amazon-fine-foods-visualization/

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
11. Objective:
12. Given a review, determine whether the review is positive (Rating of 4 or 5) or negative (rating of 1 or 2).

Text Preprocessing.

1. Begin by removing the html tags
2. Remove any punctuations or limited set of special characters like , or . or # etc.
3. Check if the word is made up of english letters and is not alpha-numeric
4. Check to see if the length of the word is greater than 2 (as it was researched that there is no adjective in 2-letters)
5. Convert the word to lowercase
6. Remove Stopwords
7. Finally Snowball Stemming the word (it was obsereved to be better than Porter Stemming)

Featurization

1. BAG OF WORDS
2. Bi-Grams and n-Grams.
3. TF-IDF
4. Word2Vec