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Luigi_docker
WebApplication8
luigi
AmazonExploratoryAnalysis.R
Dockerfile
FeatureEngineering_Prediction_of_Helpfulness.ipynb
README.md
Team11_FinalProject_Report.docx
data_conversion.ipynb
data_download.ipynb
finefoods_luigi.py
negative_words.txt
positive_words.txt
review-classification.ipynb
reviews_preprocessing_feature-extraction_classification.ipynb
team11_finalproject_presentation.pptx
top_good_worst_reviews.R

README.md

DataScience_FinalProject_AmazonFineFoodSentimentAnalysis

Team11_FinaProject_Report

=================================

Purpose of this project is to achieve sentiment classification of amazon fine food reviews Below are the steps involved in it:

  • Data Extraction and Conversion
  • Cleaning and pre-processing
  • Data Exploratory analysis (using python and Power BI)
  • Sentiment classification and analysis
  • Luigi Pipeline
  • Dockerization
  • Creation of Rest API using Microsoft Azure ML Studio
  • UI deployment

Following files are present

  • Team11_FinaProject_Report.docx It contains detailed report for project
  • team11_finalproject_presentation.pptx It contains presnetation for a final project
  • Data download data_download.ipynb
  • Data conversion data_conversion.ipynb
  • Exploratory analysis AmazonExploratoryAnalysis.R and
  • Classification of review and sentiment analysis review-classification.ipynb and review_preprocessing-feature-extraction_classification.ipynb
  • Top worst and good reviews top_good_worst_reviews.R
  • Luigi_docker folder contains script finefoods_luigi.py and Dockerfile
  • luigi folder contains script for tasks
  • WebApplication8 folder code related to UI and service deployment