I implemented a simple n-gram model for text prediction. The data consists of over 3 million tweets, blog and news posts. Using the frequencies of the bigrams, trigrams, and quadrigrams in the data, I made three n-gram prediction models. To predict the most likely next word in a given sequence, each model makes a prediction. If two or more of the n-grams models agree, I nominate that word as the most likely next word. If the models don’t agree, I use a normalized, weighted likelihood, which I calculated with the validation data.

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