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-# TwitterBotDetection
-
-### Twitonomy : 
-https://www.twitonomy.com/dashboard.php
-
-### GET users/show : 
-https://developer.twitter.com/en/docs/accounts-and-users/follow-search-get-users/api-reference/get-users-show
-
-### GET statuses/user_timeline : 
-https://developer.twitter.com/en/docs/tweets/timelines/api-reference/get-statuses-user_timeline
-
-### User Object : 
-https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object
-
-### Tweet Object : 
-https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object
-
-### Entities Object : 
-https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/entities-object
-
-### Python Twitter Tools : 
-https://github.com/sixohsix/twitter/blob/master/twitter/api.py
-
-### Twitter Python API official : 
-https://python-twitter.readthedocs.io/en/latest/twitter.html
-
-### Twitter python API reference (Tweepy) : 
-http://docs.tweepy.org/en/latest/api.html
-
-### Tweet Scraping : 
-https://pypi.org/project/GetOldTweets3/
-### Unused scraper : 
-https://github.com/5hirish/tweet_scrapper/blob/master/USAGE.md
-
-### Training data : 
-https://botometer.iuni.iu.edu/bot-repository/datasets.html
-
-
-## FILES
-
-1. Content_Data.csv - content features file
-2. Graph_Data.csv - graph features file
-3. Training_Data.csv - final, merged features for training the model
-4. MergingTrainingData (py) - code used to merge features
-5. RVMTrainingModel (py) - code for training the model, training model used : RVM with a linear kernel, testing accuracy : 81.25%
-
-
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