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
- 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
Given a review, determine whether the review is positive (Rating of 4 or 5) or negative (rating of 1 or 2).
You need to have installed following softwares and libraries before running this project.
- Python 3: https://www.python.org/downloads/
- 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/
-
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
• Manish Vishwakarma - Complete work