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README.md

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 ## Dimitri Kourouniotis Data Scientist
 
 Analysing FCC Net Neutrality Comments using Machine Learning and NLP
+
+Supervised Machine Learning using NLP
+by Dimitri Kourouniotis
+In the winter of 2017 there were numerous articles about quantity of fake comments submitted regarding the repeal of Net Neutrality laws by the FCC.
+
+A blog post published by Jeff Kao caught my attention and I followed up with him on his analysis of the text. He provided me with the unedited 22 million filings available. I analyzed a sample of 3 million of them to see what I could find to develop my own features based around the text of faked comments.
+
+00 Summary and Table of Contents
+01 Importing 3 million FCC records from SQL
+02 Email domains
+03 WordCloud
+04 Submission Frequency
+05 State Population Estimates 2016 and Comment Percentages
+06 Plotting Differences from Average
+07 Chorogrid Map of US
+08 Statistics Proportions by State Relative to Population
+09 Classifiers and Feature Selection
+Acknowledgements
+Many thanks to my mentor, Rajiv Shah!
+Thanks to the following for the data and code help for this capstone:
+Data: Jeff Kao
+More than a million pro-repeal net neutrality comments were likely faked
+https://hackernoon.com/more-than-a-million-pro-repeal-net-neutrality-comments-were-likely-faked-e9f0e3ed36a6
+
+Word Cloud: Nikhil Kumar Singh
+wordcloud example
+https://github.com/nikhilkumarsingh/wordcloud-example/blob/7a77e97c4da135b67ad924be96269d6bb68a0fe6/mywc.py
+
+Chorogrid Plot: lavinben88
+chorogrid tutorial part 2
+https://plot.ly/~lavinben88/116/chorogrid-tutorial-part-2-chorogri/
+
+Classifier Iterator: Evgeny Volkov
+SMS spam detection with various classifiers
+https://www.kaggle.com/muzzzdy/sms-spam-detection-with-various-classifiers