Welcome to the presentation on our Predictive Text Algorithm. This system uses statistical modeling to predict the next word in a sequence of words, enhancing typing efficiency and accuracy.

The algorithm is based on the n-gram model, a common approach in natural language processing (NLP). An n-gram is a contiguous sequence of n items from a given sample of text or speech. The items can be phonemes, syllables, letters, words, or base pairs according to the application.

• The n-gram model is trained on a corpus of text data.
• The corpus of size:

## Corpus length: 569577

• The dataset is preprocessed to remove punctuation, special characters, and other noise. https://drive.google.com/file/d/1GeUzNVqiixXHnTl8oNiQ2W3CynX_lsu2/view

• The n-gram model is a statistical language model that predicts the probability of the next word in a sequence given the previous n words.
• It works by analyzing the frequency of word sequences (n-grams) in the training data.
• For example, a bigram model (n=2) predicts the next word based on the previous word, while a trigram model (n=3) considers the two previous words.

• The ShinyApp consists of a simple user interface where users can input a sequence of words.
• The app uses an n-gram model trained on a specific corpus to predict the next word in the sequence.
• Overview of the Shiny App:
  • Users type into a text box, and the app dynamically predicts the next word.
  • Predictions update with each character typed.

• User Experience:
  • Users experience seamless prediction with our app, improving text composition efficiency.
  • You can test in by clicking the link below: https://alibekproj.shinyapps.io/WordPrediction/
• Conclusion:
  • Our app offers a user-friendly interface and accurate predictions, paving the way for enhanced text analysis.