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File Summary

f_names f_lines n_char n_words pct_chars pct_lines pct_words
blogs 899288 208361438 37334131 0.54 0.27 0.53
news 77259 15683765 2643969 0.04 0.02 0.04
twitter 2360148 162385035 30373583 0.42 0.71 0.43 
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## Joining, by = "word"

Uni-gram Distributions by Source

Bi-gram Distribution

Prediction Model

I will be using the table created for bi-grams as the basis for prediction. The user will input a word and the model will find the bi-gram with the greatest relative frequency given that word. The second word in this bi-gram will be the prediction of the model for the next word, given the userโ€™s input word.