ML-Notebooks

Amazon Fine Food Reviews Analysis¶

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

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).

Prerequisites

You need to have installed following softwares and libraries before running this project.

1. Python 3: https://www.python.org/downloads/
2. Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy and scipy: https://www.anaconda.com/download/

Libraries

• scikit-learn: scikit-learn is a Python module for machine learning built on top of SciPy.

  • pip install scikit-learn
  • conda install -c anaconda scikit-learn

• nltk: The Natural Language Toolkit (NLTK) is a Python package for natural language processing.

  • pip install nltk
  • conda install -c anaconda nltk

Authors

• Manish Vishwakarma - Complete work