# nytimes_resistance_letter
Which senior cabinet wrote nytimes 'resistance' article?
Below ranking of each cabinent member. Azar, Acosta, or Haley are most likely to have written article based on data analysis.
['rick perry', 1]
['Kelly_John', 2]
['kirstjen nielsen ', 2]
['mulvaney', 3]
['coats', 3]
['gina haspel', 4]
['mnuchin', 5]
['elaine chao', 5]
['mcmahon', 6]
['mike pomeo', 7]
['sonny perdue', 8]
['mattis', 8]
['zinke', 8]
['ben carson', 8]
['wilbur ross', 8]
['devos', 9]
['Robert Lighthizer', 9]
['sessions', 10]
['Robert L. Wilki', 10]
['haley', 10]
['acosta', 11]
['azar', 16]
I scraped each person's opening testimony as a dataset. I then split each person testimony into sentences. I built a model to predict who wrote a sentence given it's features (bigrams of words and characters).
Applying model to each sentence of resistance article gave a probability that a cabinent member wrote that sentence.
I set a threshold of .1 for each sentence probability; if a person recieved a probability greater than .1 then they were giving a 1 else 0 for that particular sentence. The above ranking is the sum of those scores.
code
Which senior cabinet wrote nytimes 'resistance' article?
Below ranking of each cabinent member. Azar, Acosta, or Haley are most likely to have written article based on data analysis.
['rick perry', 1]
['Kelly_John', 2]
['kirstjen nielsen ', 2]
['mulvaney', 3]
['coats', 3]
['gina haspel', 4]
['mnuchin', 5]
['elaine chao', 5]
['mcmahon', 6]
['mike pomeo', 7]
['sonny perdue', 8]
['mattis', 8]
['zinke', 8]
['ben carson', 8]
['wilbur ross', 8]
['devos', 9]
['Robert Lighthizer', 9]
['sessions', 10]
['Robert L. Wilki', 10]
['haley', 10]
['acosta', 11]
['azar', 16]
I scraped each person's opening testimony as a dataset. I then split each person testimony into sentences. I built a model to predict who wrote a sentence given it's features (bigrams of words and characters).
Applying model to each sentence of resistance article gave a probability that a cabinent member wrote that sentence.
I set a threshold of .1 for each sentence probability; if a person recieved a probability greater than .1 then they were giving a 1 else 0 for that particular sentence. The above ranking is the sum of those scores.
code
