Simran Nagpurkar's repositories
AMAZON_FINE_FOOD_REVIEWS
Amazon Fine Food Reviews Analysis 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: 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). [Q] How to determine if a review is positive or negative? [Ans] We could use Score/Rating. A rating of 4 or 5 can be cosnidered as a positive review. A rating of 1 or 2 can be considered as negative one. A review of rating 3 is considered nuetral and such reviews are ignored from our analysis. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.
Chicago-Food-Inspection-Data-Analysis
Desigining Advanced Data Architectures for Business Intelligence.
Crop-and-Fertilizer-Recommandation-System-using-FLASK
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Dallas-Food-Inspection
Desigining Advanced Data Architectures for Business Intelligence.
London-Bike-Rides-Analysis
Tableau Dashboard
NYC-Food-Inspection
Desigining Advanced Data Architectures for Business Intelligence.
Quora-Questions-Classification-NLP
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ServiceNow-Stock-Prediction
Machine Learning & Python in Finance