The word predicting app is created by the n-gram models. An n-gram model is a type of probabilistic language model for predicting the next item in such a sequence in the form of a \( (n - 1) \)-order Markov model.
The probability of a word is conditioned on some number of previous words (one word in a bigram model, two words in a trigram model, etc.). The conditional probability can be calculated from n-gram frequency counts:
\( P(w_i|w_{i-(n-1)},\ldots,w_{i-1}) = \frac{count(w_{i-(n-1)},\ldots,w_{i-1},w_i)}{count(w_{i-(n-1)},\ldots,w_{i-1})} \)
To save computation time, only 5% of the data from the data-set is used in this application.
